Wireless location gateway and applications therefor

ABSTRACT

A system for wirelessly locating mobile station/units (MS) and using resulting location determinations for providing a product or service is disclosed. The system is useful for routing an MS user to a plurality of desired locations, alerting an MS user to a nearby desired product or service based on satisfaction of user criteria, and providing enhanced security and 911 response. In one embodiment, the system responds to MS location requests via, e.g., Internet communication between a distributed network of location processing sites. A plurality of locating technologies including those based on: (1) TDOA; (2) pattern recognition; (3) timing advance; (5) GPS and network assisted GPS, (6) angle of arrival, (7) super resolution enhancements, and (8) supplemental information from low cost base stations can be activated, in various combinations, by system embodiments. MS location difficulties resulting from poor location accuracy/reliability and/or poor coverage are alleviated via such technologies in combination with automatically adapting and calibrating system performance according to environmental and geographical changes so that the system becomes progressively more comprehensive and accurate. Further, the system can be modularly configured for use in location signaling environments ranging from urban, dense urban, suburban, rural, mountain to low traffic or isolated roadways. Accordingly, the system is useful for 911 emergency calls, tracking, routing, people and animal location including applications for confinement to and exclusion from certain areas.

The present application:

-   -   is the U.S. National Stage filing of International Application        No. PCT/US01/17957 filed Jun. 4, 2001; and    -   is a continuation-in-part of U.S. application Ser No. 09/299,115        filed Apr. 23, 1999 (now U.S. Pat. No. 6,249,252); and    -   is a continuation-in-part of U.S. application Ser No. 09/176,587        filed Oct. 21, 1998; and    -   is a continuation-in-part of U.S. application Ser No. 09/194,367        filed Nov. 24, 1998; and        the above-identified International Application No.        PCT/US01/17957 claims the benefit of U.S. Provisional        Application No. 60/209,278 filed Jun. 2, 2000, and U.S.        Provisional Application No. 60/293,094 filed May 22, 2001;        U.S. application Ser. No. 09/299,115 (now U.S. Pat. No.        6,249,252):    -   is a continuation-in-part of U.S. application Ser No. 09/176,587        filed Oct. 21, 1998; and    -   is a continuation-in-part of U.S. application Ser No. 09/194,367        filed Nov. 24, 1998; and    -   is a continuation-in-part of U.S. application Ser No. 09/230,109        filed Jan. 22, 1999 (now U.S. Pat. No. 6,236,365); and    -   claims the benefit of U.S. Provisional Application No.        60/083,041 filed Apr. 23, 1998;        U.S. application Ser. No. 09/176,587:    -   claims the benefit of U.S. Provisional 06/062,931, filed Oct.        21, 1997;        U.S. application Ser. No. 09/194,367:    -   is the National Stage of International Application No.        PCT/US97/15892, filed Sep. 8, 1997 which claims the benefit of        the following three provisionals:        U.S. Provisional Application No. 10/297,449; Response to Office        Action dated Feb. 23, 2006.    -   Application No. 60/056,590 filed Aug. 20, 1997; U.S. Provisional        Application No. 60/044,821 filed Apr. 25, 1997; and U.S.        Provisional Application No. 60/025,855 filed Sep. 9, 1996;        U.S. Application No. 09/230,109 (now U.S. Pat. No. 6,236,365):    -   is the National Stage of International Application No.        PCT/US97/15933 filed Sep. 8, 1997 which claims the benefit of        the following three provisionals: U.S. Provisional Application        No. 60/056,603 filed Aug. 20, 1997; U.S. Provisional Application        No. 60/044,821 filed Apr. 25, 1997; and U.S. Provisional        Application No. 60/025,855 filed Sep. 9, 1996.

FIELD OF THE INVENTION

The present invention is directed generally to a system and method forlocating people, services, or objects, and in particular, to a systemand method for locating a wireless mobile station/unit using variousmobile station location estimators, wherein, e.g., a resulting locationdetermination(s) is used for assisting in accessing a product orservice. The present invention is additionally directed to acomputational system and method for calibrating the relative performanceof multiple location models, wherein each such model is capable of beingactivated for generating hypotheses (e.g., estimates and/or predictions)of an unknown condition such as the location of wireless mobile station.

BACKGROUND OF THE INVENTION

There is great interest in providing existing infrastructures forwireless communication systems with the capability for locating peopleand/or objects in a cost effective manner. Such a capability would beinvaluable in a variety of situations, especially in emergency, crimesituations and mobile commerce. There are numerous competing wirelesslocation technologies that purport to effectively locate wireless mobilestations (as used herein this term includes, e.g., mobile phones, shortmessage devices (SMS), electronic container tracking tags,micro-transceivers for personal location and/or emergency). Thesetechnologies can be generally classified as:

-   -   (a) handset centric wherein a portion of the location processing        is performed at the mobile stations, and in particular, each        such mobile station (MS) includes specialized electronics        specifically for performing location. In most cases, such        specialized electronics are for detecting and receiving        satellite (or more generally, non-terrestrial) signals that can        then be used in determining a location of the MS.    -   (b) network centric wherein the wireless communication        network(s) with which the MS is in contact handle substantially        all location specific processing. As one skilled in the art will        understand, there are various wireless location technologies        that are available such as time difference of arrival (TDOA),        time of arrival (TOA), timing advance (TA) techniques, angle of        arrival (AOA), multipath pattern matching techniques; and    -   (c) hybrid systems wherein there are specialized location        electronics at the handset, but a substantial amount of the        location processing is performed at a network site rather at the        MS. An example of such a hybrid system is what is known as        network assisted GPS systems, wherein GPS signals are obtained        at the MS (with the assistance network received information) and        GPS timing information is transmitted from the MS to the network        for performing MS location computations.

The wide variety of wireless location techniques can provide, underappropriate circumstances, the following advantages:

-   -   (a) if the techniques are used in combination, a more reliable        and accurate wireless location capability can be provided. In        particular, when an embodiment of one wireless location        technique is known to be less than satisfactory in a particular        geographic area, an alternative embodiment (or alternative        technique) can be used to obtain an MS's location(s).        Additionally, two different embodiments and/or techniques can be        applied substantially simultaneously for locating an MS. In this        latter case, a location resolver is likely needed to determine a        “most likely” resulting MS location estimate. Note, that        wireless location systems for combining wireless location        techniques is described in the following international and U.S.        patent applications which are each incorporated fully by        reference herein:        -   i. U.S. Provisional Application No. 60/025,855 filed Sep. 9,            1996        -   ii. U.S. Provisional Application No. 60/044,821, filed Apr.            25, 1997;        -   iii. U.S. Provisional Application No. 60/056,590, filed Aug.            20, 1997;        -   iv. International Application No. PCT/US97/115933 filed Sep.            8, 1997 entitled “LOCATION OF A MOBILE STATION USING A            PLURALITY OF COMMERCIAL WIRELESS INFRASTRUCTURES”        -   v. International Application No. PCT/US97/15892 filed Sep.            8, 1997; entitled “LOCATION OF A MOBILE STATION”;        -   vi. U.S. application Ser. No. 09/194,367 filed Nov. 24, 1999            entitled “Location Of A Mobile Station”;        -   vii. U.S. application Ser. No. 09/176,587 filed Oct. 21,            1998 entitled “Wireless Location System For Calibrating            Multiple Location Estimators”;        -   viii. U.S. Pat. No. 6,236,365 filed Jan. 22, 1999 entitled            “Location of a Mobile Station Using A Plurality Of            Commercial Infrastructures”;        -   ix. U.S. application Ser. No. 09/299,115 filed: Apr. 23,            1999 entitled “WIRELESS LOCATION USING MULTIPLE SIMULTANEOUS            LOCATION ESTIMATORS”; and    -   (b) if a primary wireless location technique fails (e.g., due to        an electronics malfunction), then assuming an alternative        technique is available that does not use, e.g., the        malfunctioning electronics of the primary technique, then the        alternative technique can be used for MS location.

However, the variety of wireless location techniques available is alsoproblematic for at least the following reasons:

-   -   (a) a request for an MS location can require either the        requester to know the wireless location service provider of the        geographical area where the MS is likely to be, or to contact a        location broker that is able to, e.g., determine a communication        network covering the geographical area within which the MS is        currently residing and activate (directly or through the MS's        wireless service provider) an appropriate wireless location        service. In the art, the technology enabling such a location        broker capability has been referred to as a “wireless location        gateway”. An embodiment of such a gateway is described in the        PCT/US97/15892 reference identified above;    -   (b) for communication networks relying on handset centric and/or        hybrid systems for MS location, MSs roaming from networks using        only network centric location capabilities will likely not have        the specialized electronics needed for being located and        accordingly many location related network services will not be        available such as emergency services (e.g., E911 in the U.S.).    -   (c) different location techniques have different reliability and        accuracy characteristics.

Accordingly, it would be desirable to integrate into a single wirelesslocation broker or wireless location gateway as many location techniquesas possible so that location requests can be fulfilled without therequester needing to know what location technique is used. It would befurther desirable for roaming MSs to be able to be located in coverageareas where a wireless location technique is different from the one (ormore) techniques supported in the primary subscription area for the MS.Additionally, it would be desirable to provide new applications forwhich MS location information can be applied via, e.g., a wirelesslocation gateway.

OBJECTS OF THE INVENTION RELATING TO WIRELESS LOCATION

It is an objective of the present invention to provide a system andmethod for accurately locating people and/or objects in a cost effectivemanner wherein a location requester can obtain an MS location withoutneeding to provide location technique specific information with therequest.

It is a further object the present invention to provide wirelesslocation without the requester knowing the particulars of communicationnetwork with which the MS may be in contact, e.g., the commercial radioservice provider (CMRS), the wireless communications protocol, etc.

Yet another objective is to provide a low cost location system andmethod, adaptable to wireless telephony/Internet systems, for using aplurality of location techniques for increasing MS location accuracy andconsistency. In particular, the plurality of location techniques(embodied in “location estimators” also denoted “first order models” orFOMs herein) may be: activated according to any one or more of a numberof activation strategies such as concurrent activation (e.g., forobtaining two location estimates of an MS location), data-drivenactivation (e.g., activated when appropriate input data is available),priority activation (e.g., an attempt to activate a preferred FOM isfirst performed, and if unsuccessful, or a result unsatisfactory, thenan attempt at activating a second FOM is performed).

Yet another object is to (or be able to) integrate into a wirelesslocation gateway a large number of MS location techniques such as:

-   -   (2.1) time-of-arrival wireless signal processing techniques;    -   (2.2) timing advance techniques;    -   (2.2) time-difference-of-arrival wireless signal processing        techniques;    -   (2.3) adaptive wireless signal processing techniques having, for        example, learning capabilities and including, for instance,        artificial neural net and genetic algorithm processing;    -   (2.4) signal processing techniques for matching MS location        signals with wireless signal characteristics of known areas;    -   (2.5) conflict resolution techniques for resolving conflicts in        hypotheses for MS location estimates;    -   (2.6) techniques for enhancing MS location estimates through the        use of both heuristics and historical data associating MS        wireless signal characteristics with known locations and/or        environmental conditions;    -   (2.7) angle of arrival techniques (also denoted direction of        arrival) for estimating an angle and/or direction of wireless        signals transmitted from an MS;    -   (2.8) location techniques that use satellite signals such as GPS        signals received at the MS;    -   (2.9) hybrid wireless location techniques that combine a two or        more of the above location techniques (2.1)–(2.2) or other        wireless location techniques.    -   (2.10) Wireless location techniques that use Doppler, phase        coherency, and other signal characteristics for determining MS        location, MS velocity and MS direction of movement.

A related object is to integrate handset centric, network centric andhybrid systems so that the problems identified hereinabove aremitigated.

Note that it is an objective of the present invention to provide a “plugand play” capability for new wireless location estimators, wherein newlocation estimators can be easily incorporated into an embodiment of thepresent invention. That is, provide an interface that allowssubstantially automatic integration of new FOMs.

Yet another object is to provide novel applications for wirelesslocation that benefits from an integration of different locationtechniques.

DEFINITIONS

The following definitions are provided for convenience. In general, thedefinitions here are also defined elsewhere in this document as well.

(3.1) The term “wireless” herein is, in general, an abbreviation for“digital wireless”, and in particular, “wireless” refers to digitalradio signaling using one of standard digital protocols such as AdvancedMobile Phone Service (AMPS), Narrowband Advanced Mobile Phone Service(NAMPS), code division multiple access (CDMA) and Time Division MultipleAccess (TDMA), Global Systems Mobile (GSM), and time division multipleaccess (TDMA) as one skilled in the art will understand.(3.2) As used herein, the term “mobile station” (equivalently, MS)refers to a wireless device that is at least a transmitting device, andin most cases is also a wireless receiving device, such as a portableradio telephony handset. Note that in some contexts herein instead or inaddition to MS, the following terms are also used: “personal station”(PS), and “location unit” (LU). In general, these terms may beconsidered synonymous. Note that examples of various MSs are identifiedin the Background section above.(3.3) The terms, “wireless infrastructure” (or simply “infrastructure”),denotes one or more of: (a) a network for one or more of telephonycommunication services, (b) a collection of commonly controlledtransceivers for providing wireless communication with a plurality ofMSs, (c) the wireless Internet, (d) that portion of communicationsnetwork that receives and processes wireless communications withwireless mobile stations. In particular, this infrastructure includestelephony wireless base stations (BS) such as those for radio mobilecommunication systems based on CDMA, AMPS, NAMPS, TDMA, and GSM whereinthe base stations provide a network of cooperative communicationchannels with an air interface to the MS, and a conventionaltelecommunications interface with a Mobile Switch Center (MSC). Thus, anMS user within an area serviced by the base stations may be providedwith wireless communication throughout the area by user transparentcommunication transfers (i.e., “handoffs”) between the users MS andthese base stations in order to maintain effective telephony service.The mobile switch center (MSC) provides communications and controlconnectivity among base stations and the public telephone network.(3.4) The phrase, “composite wireless signal characteristic values”denotes the result of aggregating and filtering a collection ofmeasurements of wireless signal samples, wherein these samples areobtained from the wireless communication between an MS to be located andthe base station infrastructure (e.g., a plurality of networked basestations). However, other phrases are also used herein to denote thiscollection of derived characteristic values depending on the context andthe likely orientation of the reader. For example, when viewing thesevalues from a wireless signal processing perspective of radioengineering, as in the descriptions of the subsequent DetailedDescription sections concerned with the aspects of the present inventionfor receiving MS signal measurements from the base stationinfrastructure, the phrase typically used is: “RF signal measurements”.Alternatively, from a data processing perspective, the phrases:“location signature cluster” and “location signal data” are used todescribe signal characteristic values between the MS and the pluralityof infrastructure base stations substantially simultaneously detectingMS transmissions. Moreover, since the location communications between anMS and the base station infrastructure typically include simultaneouscommunications with more than one base station, a related useful notionis that of a “location signature” (also denoted “loc sig” herein) whichis the composite wireless signal characteristic values for signalsamples between an MS to be located and a single base station. Also, insome contexts, the phrases: “signal characteristic values” or “signalcharacteristic data” are used when either or both a locationsignature(s) and/or a location signature cluster(s) are intended.

SUMMARY DISCUSSION

The present invention relates to a method and system for performingwireless mobile station location. In particular, the present inventionis a wireless mobile station location computing method and system thatutilizes multiple wireless location computational estimators (theseestimators also denoted herein as MS location hypothesizingcomputational models, “first order models”, FOMs, and/or “locationestimating models”), for providing location estimates of a target mobilestation MS, wherein ambiguities and/or conflicts between the locationestimates may be effectively and straightforwardly resolved. Moreparticularly, the present invention provides a technique for calibratingthe performance of each of the location estimators so that a confidencevalue (e.g., a probability) can be assigned to each generated locationestimate. Additionally, the present invention provides a straightforwardtechnique for using the confidence values (probabilities) for deriving aresulting most likely location estimate of a target wireless mobilestation.

More generally, the present invention relates to a novel computationalmethod and architecture for synergistically combining the results of aplurality of computational models in a straightforward way that allowsthe models to be calibrated relative to one another so that differencesin results generated by the models can be readily resolved. Accordingly,the computational method and architecture of the present invention maybe applied to wide range applications where synergies between multiplemodels is expected to be enhance performance.

For a particular application having a plurality of computational models(each generating a hypothetical estimate of a desired result(s) in aspace of hypothesis results), the present invention may be described, ata high level, as any method or system that performs the following steps:

-   -   (4.1.1) A step of determining a classification scheme for        determining an input class for each input data set supplied to        the plurality of computational models (FOMs), wherein for each        range, R, of a plurality of ranges of desired results in the        hypothesis space, there is an input class, and the input data        sets of this input class are expected to have their        corresponding desired result(s) in the range R. Some examples        will be illustrative. For a wireless location system, the        present step determines geographical subareas of a wireless        network coverage area that have “similar” wireless signal        characteristics. Such subareas may be relatively easy to        determine, and there may be no constraint on the size of the        subareas. The intention is to determine: (a) such a subarea as        only a general area where a target MS must reside, and (b) the        subarea should be relatively homogeneous in its wireless        signaling characteristics. Accordingly, (a) and (b) are believed        to be substantially satisfied by grouping together into the same        input class the wireless signal data sets (i.e., input data        sets) from corresponding target MS locations wherein at each of        the target MS locations: (i) the set of base stations detected        by the target MS (at the location) is substantially the same,        and/or (b) the set of base stations detecting the target MS is        substantially the same set of base stations.        -   Note that there are numerous techniques and commercial            packages for determining such a classification scheme. In            particular, the statistically based system, “CART” (acronym            for Classification and Regression Trees) by ANGOSS Software            International Limited of Toronto, Canada is one such            package. Further, note that this step is intended to provide            reliable but not necessarily highly accurate ranges R for            the desired results. Also note that in some applications            there may be only a single input class, thus assuring high            reliability (albeit, likely low accuracy). Accordingly, in            this latter case the present step may be omitted.    -   (4.1.2) A step of calibrating each of the plurality of        computational models (FOMs) so that each subsequent hypothesis        generated by one of the models has a confidence value (e.g.,        probability) associated therewith that is indicative of the        likeliness of the hypothesis being correct. The calibrating of        this step is performed using the input classification scheme        determined in the above step (4.1.1). In one embodiment of this        step, each model is supplied with inputs from a given fixed        input class, wherein each of these inputs have corresponding        known results that constitute a correct hypothesis (i.e., a        desired result). Subsequently, the performance of each model is        determined for the input class and a confidence value is        assigned to the model for inputs received from the input class.        Note that this procedure is repeated with each input class        available from the input classification scheme. In performing        this procedure, an application domain specific criteria is used        to determine whether the hypotheses generated by the models        identify the desired results in the hypothesis space.        Accordingly, for each of the models, when supplied with an input        data set from a fixed input class, the hypothesis generated by        the model will be given the confidence value determined for this        input class as an indication of the likelihood of the generated        hypothesis being correct (i.e., the desired result). Note that        the confidence value for each generated hypothesis may be        computed as a probability that the hypothesis is correct.        -   Note that for a wireless location application, the criteria            (in one embodiment) is whether a location hypothesis            contains the actual location where the MS was when the            corresponding input data set (wireless signal measurements)            were communicated between this MS and the wireless network.        -   For applications related to the diagnosis of electronic            systems, this criteria may be whether an hypothesis            identifies a proper functional unit such as a circuit board            or chip.        -   For economic forecasting applications, this criteria may be            whether an hypothesis is within a particular range of the            correct hypothesis. For example, if an application according            to the present invention predicts the U.S. gross national            product (GNP) six months into the future according to            certain inputs (defining input data sets), then hypotheses            generated from historical data that has associated therewith            the actual corresponding GNP (six months later), may be used            for calibrating each of the plurality of economic            forecasting models (FOMs). Thus, the application specific            criteria for this case may be that a generated hypothesis is            within, say, 10% of the actual corresponding six month GNP            prediction.        -   For identifying a known object such as an air or space            borne, terrestrial vehicle, or watercraft, the criteria may            be whether an hypothesis actually identifies the object.        -   For geophysical analysis applications (e.g., for identifying            and/or classifying and/or mapping mineral deposits, oil,            aquifers or seismic faults), the criteria may be whether an            hypothesis provides a correct analysis.        -   Note that the applications described herein are            illustrative, but not comprehensive of the scope of the            present invention. Further note that this step typically is            performed at least once prior to inputting input data sets            whose resulting hypotheses are to be used to determine the            desired or correct results. Additionally, once an initial            calibration has been performed, this step may also be            performed: (a) intermittently between the generation of            hypotheses, and/or (b) substantially continuously and in            parallel with the generation of hypotheses by the models.    -   (4.1.3) A step of providing one or more input data sets to the        models (FOMs) for generating a plurality of hypotheses, wherein        the result(s) desired to be hypothesized are unknown. Moreover,        note that the generated hypotheses are preferred to have a same        data structure definition.        -   For example, for a wireless location system, the present            step provides an input data set including the composite            signal characteristic values to one or more MS location            hypothesizing computational models, wherein each such model            subsequently determines one or more initial estimates (also            denoted location hypotheses) of the location of the target            MS. Note that one or more of these model may be based on,            for example, the signal processing techniques 2.1 through            2.3 above.    -   (4.1.4) A step of adjusting or modifying the generated        hypotheses output by the models, wherein for such an hypothesis,        adjustments may be performed on one or both of its hypothesized        result H.R, and its confidence value for further enhancing the        performance of the present invention. In one embodiment of this        step, H.R is used as an index to retrieve other results from an        archival database, wherein this database associates hypothesized        results with their corresponding desired or correct results.        Thus, H.R may be used to identify data from other archived        hypothesized results that are “nearby” to H.R, and subsequently        use the nearby data to retrieve the corresponding desired        results. Thus, the set of retrieved desired results may be used        to define a new “adjusted” hypothesis.        -   For example, for a wireless location system utilizing the            present invention, each location hypothesis, H, identifies            an area for a target MS, and H can used to identify            additional related locations included in archived hypotheses            generated by the same FOM as generated H. For instance, such            related locations may be the area centroids of the archived            hypotheses, wherein these centroids reside within the area            hypothesized by H. Accordingly, such centroids may be used            to retrieve the corresponding actual verified MS locations            (i.e., the corresponding desired results), and these            retrieved verified locations may be used to generate a new            adjusted area that is likely to be more accurate than H. In            particular, a convex hull of the verified locations may be            used as a basis for determining a new location hypothesis of            the target MS. Moreover, this aspect of the invention may            include the preprocessing of such adjustments throughout a            wireless coverage area to produce a geolocation vector            gradient field, wherein for each archived hypotheses H            (having L_(H) as an MS location estimate) for a designated            FOM, throughout the coverage area, a corresponding verified            location version VL_(H) is determined. Subsequently, the            adjustment vector AV_(H)=(VL_(H)−L_(H)) is determined as one            of the adjustment vectors of the vector gradient field.            Thus, L_(H) and AV_(H) are associated in the data archive as            a record of the vector gradient field. Accordingly, when a            location hypothesis H0 for a target MS at an unknown            location is generated (the hypothesis H0 having L0 as the            target MS location estimate), records within the vector            gradient field having their corresponding location L_(H)            “near” L0, (e.g., within area of a predetermined distance            about L0 or a “neighborhood: of L0) can be retrieved.            Accordingly, an adjustment to L0 can be determined as a            function of the L_(H) and AV_(H) values of the retrieved            records. Note that an adjustment to L0 may be simply an            average of these AV_(H) vectors for the retrieved records.            Alternatively, the AV_(H) values may be weighted such that            the AV_(H) having L_(H) closer to L0 are more influential in            the resulting derived location for the target MS. More            generally, the adjustment technique includes a method for            interpolating an adjustment at L0 from the verified            adjustments at locations about L0. Enhancements on such            adjustment/interpolation techniques are also within the            scope of the present invention. For example, the weightings            (or other terms of an such an interpolation technique) may            be combined with other known wireless signal characteristics            of the area such as an identification of: (a) a known sharp            change in the geolocation gradient vector field, and/or (b)            a subarea having reduced wireless transmission capabilities,            and/or (c) a subarea wherein the retrieved records for the            subarea have their estimates L_(H) widely spaced apart,            and/or (d) a subarea wherein there is an insufficient number            of retrieved records.        -   For other application domains, the present step requires a            first technique to determine both “nearby” archived data            from previously archived hypotheses, and a second technique            to determine an “adjusted” hypothesis from the retrieved            desired results. In general, such techniques can be            relatively straightforward to provide when the hypothesized            results reside in a vector space, and more particularly, in            a Cartesian product of the real numbers. Accordingly, there            are numerous applications that can be configured to generate            hypothesized results in a vector space (or Cartesian product            of the real numbers). For instance, economic financial            forecasting applications typically result in numeric            predictions where the first and second techniques can be,            e.g., substantially identical to the centroid and convex            hull techniques for the wireless location application; and    -   (4.1.5) A step of subsequently computing a “most likely” target        MS location estimate is computed for outputting to a location        requesting application such as 911 emergency, the fire or police        departments, taxi services, etc. Note that in computing the most        likely target MS location estimate a plurality of location        hypotheses may be taken into account. In fact, it is an        important aspect of the present invention that the most likely        MS location estimate is determined by computationally forming a        composite MS location estimate utilizing such a plurality of        location hypotheses so that, for example, location estimate        similarities between location hypotheses can be effectively        utilized.

Referring to (4.1.3) there may be hypotheses for estimating not onlydesired result(s), but also hypotheses may be generated that indicatewhere the desired result(s) is not. Thus, if the confidence values areprobabilities, an hypothesis may be generated that has a very low (nearzero) probability of having the desired result. As an aside, note thatin general, for each generated hypothesis, H, having a probability, P,there is a dual hypothesis H^(c) that may be generated, wherein theH^(c) represents the complementary hypothesis that the desired result isin the space of hypothesized results outside of H. Thus, the probabilitythat the desired result(s) is outside of the result hypothesized by H is1-P. Accordingly, with each location hypothesis having a probabilityfavorably indicating where a desired result may be (i.e., P>=0.5), thereis a corresponding probability for the complement hypothesis thatindicates where the desired result(s) is unlikely to be. Thus, applyingthis reasoning to a wireless location application utilizing the presentinvention, then for an hypothesis H indicating that the target MS is ina geographical area A, there is a dual location estimate H^(c) that maybe generated, wherein the H^(c) represents the area outside of A and theprobability that the target MS is outside of A is 1−P. Thus, with eachlocation hypothesis having a probability favorably indicating where atarget MS may be (i.e., P>=0.5), there is a corresponding probabilityfor the complement area not represented by the location hypothesis thatdoes not favor the target MS being in this complement area. Further,note that similar dual hypotheses can be used in other applicationsusing the multiple model architecture of the present invention whenprobabilities are assigned to hypotheses generated by the models of theapplication.

Referring to (4.1.3) as it relates to a wireless location systemprovided by the present invention, note that, it is an aspect of thepresent invention to provide location hypothesis enhancing andevaluation techniques that can adjust target MS location estimatesaccording to historical MS location data and/or adjust the confidencevalues of location hypotheses according to how consistent thecorresponding target MS location estimate is: (a) with historical MSsignal characteristic values, (b) with various physical constraints, and(c) with various heuristics. In particular, the following capabilitiesare provided by the present invention:

-   -   (5.1) a capability for enhancing the accuracy of an initial        location hypothesis, H, generated by a first order model,        FOM_(H), by using H as, essentially, a query or index into an        historical data base (denoted herein as the location signature        data base). Note, this data base may include: (a) a plurality of        previously obtained location signature clusters (i.e., composite        wireless signal characteristic values) such that for each such        cluster there is an associated actual or verified MS locations        where an MS communicated with the base station infrastructure        for locating the MS, and (b) previous MS location hypothesis        estimates from FOM_(H) derived from each of the location        signature clusters stored according to (a). Alternatively this        data base include a location error gradient field for the know        location errors for FOM_(H);    -   (5.2) a capability for analyzing composite signal characteristic        values of wireless communications between the target MS and the        base station infrastructure, wherein such values are compared        with composite signal characteristics values of known MS        locations (these latter values being archived in the location        signature data base). In one instance, the composite signal        characteristic values used to generate various location        hypotheses for the target MS are compared against wireless        signal data of known MS locations stored in the location        signature data base for determining the reliability of the        location hypothesizing models for particular geographic areas        and/or environmental conditions;    -   (5.3) a capability for reasoning about the likeliness of a        location hypothesis wherein this reasoning capability uses        heuristics and constraints based on physics and physical        properties of the location geography;    -   (5.4) an hypothesis generating capability for generating new        location hypotheses from previous hypotheses.

As also mentioned above in (2.3), the present invention may utilizeadaptive signal processing techniques. One particularly importantutilization of such techniques includes the automatic tuning of thepresent invention so that, e.g., such tuning can be applied to adjustingthe values of location processing system parameters that affect theprocessing performed by the present invention. For example, such systemparameters as those used for determining the size of a geographical areato be specified when retrieving location signal data of known MSlocations from the historical (location signature) data base cansubstantially affect the location processing. In particular, a systemparameter specifying a minimum size for such a geographical area may, iftoo large, cause unnecessary inaccuracies in locating an MS.Accordingly, to accomplish a tuning of such system parameters, anadaptation engine is included in the present invention for automaticallyadjusting or tuning parameters used by the present invention. Note thatin one embodiment, the adaptation engine is based on genetic algorithmtechniques.

The present invention may include one or more FOMs that may be generallydenoted as classification models wherein such FOMs are trained orcalibrated to associate particular composite wireless signalcharacteristic values with a geographical location where a target MScould likely generate the wireless signal samples from which thecomposite wireless signal characteristic values are derived. Further,the present invention may include the capability for training andretraining such classification FOMs to automatically maintain theaccuracy of these models even though substantial changes to the radiocoverage area may occur, such as the construction of a new high risebuilding or seasonal variations (due to, for example, foliagevariations). As used herein, “training” refers to iteratively presenting“training data” to a computational module for changing the behavior ofthe module so that the module may perform progressively better as itlearns appropriate behavioral responses to the training data.Accordingly, training may include, for example, the repeated input oftraining data to an artificial neural network, or repeated statisticalregression analyses on different and/or enhanced training data (e.g.,statistical sample data sets). Note that other embodiments of a trainedpattern matching FOMs for wireless location are disclosed in U.S. Pat.No. 6,026,304, titled “Radio Transmitter Location Finding for WirelessCommunication Network Services and Management,” filed Jan. 8, 1997 andissued Feb. 15, 2000, having Hilsenrath and Wax as inventors, thispatent being incorporated herein fully by reference.

It is well known in the wireless telephony art that the phenomenon ofsignal multipath and shadow fading renders most analytical locationcomputational techniques such as time-of-arrival (TOA) ortime-difference-of-arrival (TDOA) substantially error prone in urbanareas and particularly in dense urban areas without further statisticalcorrelation processing such as such super resolution as disclosed inU.S. Pat. No. 5,890,068 by Fattouche et. al. issued on Mar. 30, 1999 andincorporated fully herein by reference. Moreover, it may be the casethat even though such additional processing is performed, the multipathphenomenon may still be problematic. However, this same multipathphenomenon also may produce substantially distinct or peculiar signalmeasurement patterns, wherein such a pattern coincides with a relativelysmall geographical area. Thus, the present invention may include aFOM(s) utilize multipath as an advantage for increasing accuracy.Moreover, it is worthwhile to note that the utilization ofclassification FOMs in high multipath environments is especiallyadvantageous in that high multipath environments are typically denselypopulated. Thus, since such environments are also capable of yielding agreater density of MS location signal data from MSs whose actuallocations can be obtained, there can be a substantial amount of trainingor calibration data captured by the present invention for training orcalibrating such classification FOMs and for progressively improving theMS location accuracy of such models.

It is also an aspect of the present invention that classification FOMsmay be utilized that determine target MS locations by correlating and/orassociating network anomalous behavior with geographic locations wheresuch behavior occurs. That is, network behaviors that are problematicfor voice and/or data communication may be used advantageously forlocating a target MS. For example, it is well known that wirelessnetworks typically have within their coverage areas persistent subareaswhere voice quality is problematic due to, e.g., measurements related tohigh total errors, a high error rate, or change in error rate. Inparticular, such measurements may be related to frame error rates,redundancy errors, co-channel interference, excessive handoffs betweenbase stations, and/or other call quality measurements. Additionally,measurements may be used that are related to subareas where wirelesscommunication between the network and a target MS is not sufficient tomaintain a call (i.e., “deadzones”). Thus, information about such socalled problematic behaviors may used by, e.g., a location estimator(FOM) to generate a more accurate estimate of a target MS. For example,such network behavioral measurements may be provided for training anartificial neural network and/or for providing to a statisticalregression analysis technique and/or statistical prediction models(e.g., using principle decomposition, partial least squares, or otherregression techniques) for associating or correlating such measurementswith the geographic area for which they likely derive. Moreover, notethat such network behavioral measurements can also be used to reduce thelikelihood of a target MS being in an area if such measurements are notwhat would be expected for the area.

It is also an aspect of the present invention that FOMs themselves maybe hybrid combinations of MS location techniques. For example, anembodiment of the present invention may include a FOM that uses acombination of Time Difference of Arrival (TDOA) and Timing Advance (TA)location measurement techniques for locating the target MS, wherein sucha technique may require only minor modifications to the wirelessinfrastructure. In particular, such a FOM may provide reduced MSlocation errors and reduced resolution of ambiguities than are presentwhen these techniques are used separately. One embodiment of such a FOM(also denoted the Yost Model or FOM herein) is disclosed in U.S. Pat.No. 5,987,329 filed Jul. 30, 1997 and issued Nov. 16, 1999 having Yostand Panchapakesan as inventors, this patent being fully incorporatedherein by reference.

Additionally, note that FOMs related to the Yost Model may also beincorporated into embodiments of the present invention wherein anelliptical search restriction location technique may also be utilized.In particular, such a technique is disclosed in U.S. patent application,having U.S. Ser. No. 08/903,551, and entitled “System and Method UsingElliptical Search Area Coverage in Determining the Location of a MobileTerminal”, filed Jul. 30, 1997, which is also incorporated by referenceherein.

It is also a related aspect of the present invention to include aplurality of stationary, low cost, low power “location detection basestations” (LBS), each such LBS having both restricted range MS detectioncapabilities, and a built-in MS. Accordingly, a grid of such LBSs can beutilized for providing wireless signaling characteristic data (fromtheir built-in MSs) for: (a) (re)training such classification FOMs, and(b) calibrating the FOMs so that each generated location hypothesis hasa reliable confidence value (probability) indicative of the likelinessof the target MS being in an area represented by the locationhypothesis.

It is a further aspect of the present invention that the personalcommunication system (PCS) infrastructures currently being developed bytelecommunication providers offer an appropriate localizedinfrastructure base upon which to build various personal locationsystems (PLS) employing the present invention and/or utilizing thetechniques disclosed herein. In particular, the present invention isespecially suitable for the location of people and/or objects using codedivision multiple access (CDMA) wireless infrastructures, although otherwireless infrastructures, such as, time division multiple access (TDMA)infrastructures and GSM are also contemplated. CDMA general principlesare described, for example, in U.S. Pat. No. 5,109,390, to Gilhausen, etal, which is also incorporated herein by reference.

As mentioned in (1.7) and in the discussion of classification FOMsabove, embodiments of the present invention may include components(e.g., FOMs) that can substantially automatically retrain themselves tocompensate for variations in wireless signal characteristics (e.g.,multipath) due to environmental and/or topographic changes to ageographic area serviced by the present invention. For example, in oneembodiment, the present invention optionally includes low cost, lowpower base stations, denoted location base stations (LBS) above,providing, for example, CDMA pilot channels to a very limited area abouteach such LBS. The location base stations may provide limited voicetraffic capabilities, but each is capable of gathering sufficientwireless signal characteristics from an MS within the location basestation's range to facilitate locating the MS. Thus, by positioning thelocation base stations at known locations in a geographic region suchas, for instance, on street lamp poles and road signs, additional MSlocation accuracy can be obtained. That is, due to the low power signaloutput by such location base stations, for there to be signaling controlcommunication (e.g., pilot signaling and other control signals) betweena location base station and a target MS, the MS must be relatively nearthe location base station. Additionally, for each location base stationnot in communication with the target MS, it is likely that the MS is notnear to this location base station. Thus, by utilizing informationreceived from both location base stations in communication with thetarget MS and those that are not in communication with the target MS,the present invention may substantially narrow the possible geographicareas within which the target MS is likely to be. Further, by providingeach location base station (LBS) with a co-located stationary wirelesstransceiver (denoted a built-in MS above) having similar functionalityto an MS, the following advantages are provided:

(6.1) assuming that the co-located base station capabilities and thestationary transceiver of an LBS are such that the base stationcapabilities and the stationary transceiver communicate with oneanother, the stationary transceiver can be signaled by anothercomponent(s) of the present invention to activate or deactivate itsassociated base station capability, thereby conserving power for the LBSthat operate on a restricted power such as solar electrical power;(6.2) the stationary transceiver of an LBS can be used for transferringtarget MS location information obtained by the LBS to a conventionaltelephony base station;(6.3) since the location of each LBS is known and can be used inlocation processing, the present invention is able to (re)train itselfin geographical areas having such LBSs. That is, by activating each LBSstationary transceiver so that there is signal communication between thestationary transceiver and surrounding base stations within range,wireless signal characteristic values for the location of the stationarytransceiver are obtained for each such base station. Accordingly, suchcharacteristic values can then be associated with the known location ofthe stationary transceiver for training various of the locationprocessing modules of the present invention such as the classificationFOMs discussed above. In particular, such training and/or calibratingmay include:

(i) (re)training FOMs;

(ii) adjusting the confidence value initially assigned to a locationhypothesis according to how accurate the generating FOM is in estimatingthe location of the stationary transceiver using data obtained fromwireless signal characteristics of signals between the stationarytransceiver and base stations with which the stationary transceiver iscapable of communicating;

(iii) automatically updating the previously mentioned historical database (i.e., the location signature data base), wherein the stored signalcharacteristic data for each stationary transceiver can be used fordetecting environmental and/or topographical changes (e.g., a newlybuilt high rise or other structures capable of altering the multipathcharacteristics of a given geographical area); and

(iv) tuning of the location system parameters, wherein the steps of: (a)modifying various system parameters and (b) testing the performance ofthe modified location system on verified mobile station location data(including the stationary transceiver signal characteristic data), thesesteps being interleaved and repeatedly performed for obtaining bettersystem location accuracy within useful time constraints.

One embodiment of the present invention utilizes a mobile (location)base station (MBS) that can be, for example, incorporated into avehicle, such as an ambulance, police car, or taxi. Such a vehicle cantravel to sites having a transmitting target MS, wherein such sites maybe randomly located and the signal characteristic data from thetransmitting target MS at such a location can consequently be archivedwith a verified location measurement performed at the site by the mobilelocation base station. Moreover, it is important to note that such amobile location base station as its name implies also includes basestation electronics for communicating with mobile stations, though notnecessarily in the manner of a conventional infrastructure base station.In particular, a mobile location base station may (in one embodiment)only monitor signal characteristics, such as MS signal strength, from atarget MS without transmitting signals to the target MS. Alternatively,a mobile location base station can periodically be in bi-directionalcommunication with a target MS for determining a signal time-of-arrival(or time-difference-of-arrival) measurement between the mobile locationbase station and the target MS. Additionally, each such mobile locationbase station includes components for estimating the location of themobile location base station, such mobile location base station locationestimates being important when the mobile location base station is usedfor locating a target MS via, for example, time-of-arrival ortime-difference-of-arrival measurements as one skilled in the art willappreciate. In particular, a mobile location base station can include:

(7.1) a mobile station (MS) for both communicating with other componentsof the present invention (such as a location processing center includedin the present invention);

(7.2) a GPS receiver for determining a location of the mobile locationbase station;

(7.3) a gyroscope and other dead reckoning devices; and

(7.4) devices for operator manual entry of a mobile location basestation location.

Furthermore, a mobile location base station includes modules forintegrating or reconciling distinct mobile location base stationlocation estimates that, for example, can be obtained using thecomponents and devices of (7.1) through (7.4) above. That is, locationestimates for the mobile location base station may be obtained from: GPSsatellite data, mobile location base station data provided by thelocation processing center, dead reckoning data obtained from the mobilelocation base station vehicle dead reckoning devices, and location datamanually input by an operator of the mobile location base station.

The location estimating system of the present invention offers manyadvantages over existing location systems. The present invention employsa number of distinctly different location estimators which provide agreater degree of accuracy and/or reliability than is possible withexisting wireless location systems. For instance, the location modelsprovided may include not only the radius—radius/TOA and TDOA techniquesbut also adaptive techniques such as artificial neural net techniquesand the techniques disclosed in the U.S. Pat. No. 6,026,304 byHilsenrath et. al. incorporated by reference herein, and angle ordirection of arrival techniques as well as substantially any otherwireless location technique wherein appropriate input data can beobtained.

-   -   (a) Note that hybrid location estimators based on combinations        of such techniques (such as the location technique of U.S. Pat.        No. 5,987,329 by Yost et. al) may also be provided by the        present invention.

It is also an aspect of the present invention that various embodimentsmay provide various strategies for activating, within a single MSlocation instance, one or more location estimators (FOMs), wherein eachsuch activated location estimator is provided with sufficient wirelesssignal data input for the activation. In one embodiment, one suchstrategy may be called “greedy” in that substantially as many locationestimators may be activated as there is sufficient input (additionally,time and resources as well) for activation. Note that some wirelesslocation techniques are dependent on specialized location relateddevices being operational such as fixed or network based receivers,antennas, transceivers, and/or signal processing equipment. Additionallynote that some location techniques also require particular functionalityto be operable in the MS; e.g., functionality for detecting one or morelocation related signals from satellites (more generally non-terrestrialtransmitting stations). For example, the signals may be GPS signals.Accordingly, certain wireless location techniques may have theiractivations dependent upon whether such location related devices and/orMS functionality are available and operable for each instance ofdetermining an MS location. Thus, for each MS wireless locationinstance, location estimators may be activated according to the operablefeatures present during an MS location instance for providing inputactivation data.

The present invention may be able to adapt to environmental changessubstantially as frequently as desired. Thus, the present invention maybe able to take into account changes in the location topography overtime without extensive manual data manipulation. Moreover, the presentinvention can be utilized with varying amounts of signal measurementinputs. Thus, if a location estimate is desired in a very short timeinterval (e.g., less than approximately one to two seconds), then thepresent invention can be used with only as much signal measurement dataas is possible to acquire during an initial portion of this timeinterval. Subsequently, after a greater amount of signal measurementdata has been acquired, additional more accurate location estimates maybe obtained. Note that this capability can be useful in the context of911 emergency response in that a first quick coarse wireless mobilestation location estimate can be used to route a 911 call from themobile station to a 911 emergency response center that hasresponsibility for the area containing the mobile station and the 911caller. Subsequently, once the 911 call has been routed according tothis first quick location estimate, by continuing to receive additionalwireless signal measurements, more reliable and accurate locationestimates of the mobile station can be obtained.

Moreover, there are numerous additional advantages of the system of thepresent invention when applied in communication systems using, e.g.,CDMA. The location system of the present invention readily benefits fromthe distinct advantages of the CDMA spread spectrum scheme. Namely,these advantages include the exploitation of radio frequency spectralefficiency and isolation by (a) monitoring voice activity, (b)management of two-way power control, (c) provisioning of advancedvariable-rate modems and error correcting signal encoding, (d) inherentresistance to fading, (e) enhanced privacy, and (f) multiple “rake”digital data receivers and searcher receivers for correlation of signalmultipaths.

At a more general level, it is an aspect of the present invention todemonstrate the utilization of various novel computational paradigmssuch as:

-   (8.1) providing a multiple FOM computational architecture (as    illustrated in FIG. 8) wherein:    -   (8.1.1) the hypotheses may be generated by modular independent        hypothesizing computational models (FOMs), wherein the FOMs have        been calibrated to thereby output confidence values        (probabilities) related to the likelihood of correspondingly        generated hypotheses being correct;    -   (8.1.2) the location hypotheses from the FOMs may be further        processed using additional amounts of application specific        processing common or generic to a plurality of the FOMs;    -   (8.1.3) the computational architecture may enhance the        hypotheses generated by the FOMs both according to past        performance of the models and according to application specific        constraints and heuristics without requiring complex feedback        loops for recalibrating one or more of the FOMs;    -   (8.1.4) the FOMs are relatively easily integrated into, modified        and extracted from the computational architecture;-   (8.2) providing a computational paradigm for enhancing an initial    estimated solution to a problem by using this initial estimated    solution as, effectively, a query or index into an historical data    base of previous solution estimates and corresponding actual    solutions for deriving an enhanced solution estimate based on past    performance of the module that generated the initial estimated    solution.

The multiple FOM architecture provided herein is useful in implementingsolutions in a wide range of applications. In fact, most of the DetailedDescription hereinbelow can be immediately translated into otherapplication areas, as one skilled in the art of computer applicationarchitectures will come to appreciate. For example, the followingadditional applications are within the scope of the present invention:

-   (9.1) document scanning applications;-   (9.2) diagnosis and monitoring applications such as medical    diagnosis/monitoring, communication network diagnosis/monitoring.    Note that in many cases, the domain wherein a diagnosis is to be    performed has a canonical hierarchical order among the components    within the domain. For example, in automobile diagnosis, the    components of an auto may be hierarchically ordered according to    ease of replacement in combination within function. Thus, within an    auto's electrical system (function), there may be a fuse box, and    within the fuse box there will be fuses. Thus, these components may    be ordered as follows (highest to lowest): auto, electrical system,    fuse box, fuses. Thus, if different diagnostic FOMs provided    different hypotheses as to a problem with an auto, the confidence    values for each component and its subcomponents maybe summed    together to provide a likelihood value that the problem within the    component. Accordingly, the lowest component having, for example, at    least a minimum threshold of summed confidences can be selected as    the most likely component for either further analysis and/or    replacement. Note that such summed confidences may be normalized by    dividing by the number of hypotheses generated from the same input    so that the highest summed confidence is one and the lowest is zero.    Further note that this example is merely representative of a number    of different diagnosis and/or prediction applications to which the    present invention is applicable, wherein there are components that    have canonical hierarchical decompositions. For example, a technique    similar to the auto illustration above may be provided for the    diagnosis of computer systems, networks (LANs, WANs, Internet and    telephony networks), medical diagnosis from, e.g., x-rays, MRIs,    sonograms, etc;-   (9.3) robotics applications such as scene and/or object recognition.    That is, various FOMs may process visual image input differently,    and it may be that for expediency, an object is recognized if the    summed confidence values for the object being recognized is above a    certain threshold;-   (9.4) seismic and/or geologic signal processing applications such as    for locating oil and gas deposits;-   (9.5) recognition of terrestrial and/or airborne objects from    satellites, wherein there may be various spectral bands monitored.-   (9.6) Additionally, note that this architecture need not have all    modules co-located. In particular, it is an additional aspect of the    present invention that various modules can be remotely located from    one another and communicate with one another via telecommunication    transmissions such as telephony technologies and/or the Internet.    Accordingly, the present invention is particularly adaptable to such    distributed computing environments. For example, some number of the    first order models may reside in remote locations and communicate    their generated hypotheses via the Internet.

In an alternative embodiment of the present invention, the processingfollowing the generation of location hypotheses (each having an initiallocation estimate) by the first order models may be such that thisprocessing can be provided on Internet user nodes and the first ordermodels may reside at Internet server sites. In this configuration, anInternet user may request hypotheses from such remote first order modelsand perform the remaining processing at his/her node.

Additionally, note that it is within the scope of the present inventionto provide one or more central location development sites that may benetworked to, for example, geographically dispersed location centersproviding location services according to the present invention, whereinthe FOMs may be accessed, substituted, enhanced or removed dynamicallyvia network connections (via, e.g., the Internet) with a centrallocation development site. Thus, a small but rapidly growingmunicipality in substantially flat low density area might initially beprovided with access to, for example, two or three FOMs for generatinglocation hypotheses in the municipality's relatively uncluttered radiosignaling environment. However, as the population density increases andthe radio signaling environment becomes cluttered by, for example,thermal noise and multipath, additional or alternative FOMs may betransferred via the network to the location center for the municipality.

Note that in some embodiments of the present invention, since there is alack of sequencing between the FOMs and subsequent processing ofhypotheses (e.g., location hypotheses, or other application specifichypotheses), the FOMs can be incorporated into an expert system, ifdesired. For example, each FOM may be activated from an antecedent of anexpert system rule. Thus, the antecedent for such a rule can evaluate toTRUE if the FOM outputs a location hypothesis, and the consequentportion of such a rule may put the output location hypothesis on a listof location hypotheses occurring in a particular time window forsubsequent processing by the location center. Alternatively, activationof the FOMs may be in the consequents of such expert system rules. Thatis, the antecedent of such an expert system rule may determine if theconditions are appropriate for invoking the FOM(s) in the rule'sconsequent.

The present invention may also be configured as a blackboard system withintelligent agents (FOMs). In this embodiment, each of the intelligentagents is calibrated using archived data so that for each of the inputdata sets provided either directly to the intelligent agents or to theblackboard, each hypothesis generated and placed on the blackboard bythe intelligent agents has a corresponding confidence value indicativeof an expected validity of the hypothesis.

Of course, other software architectures may also to used in implementingthe processing of the location center without departing from scope ofthe present invention. In particular, object-oriented architectures arealso within the scope of the present invention. For example, the FOMsmay be object methods on an MS location estimator object, wherein theestimator object receives substantially all target MS location signaldata output by the signal filtering subsystem. Alternatively, softwarebus architectures are contemplated by the present invention, as oneskilled in the art will understand, wherein the software architecturemay be modular and facilitate parallel processing.

Further features and advantages of the present invention are provided bythe figures and detailed description accompanying this inventionsummary.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates various perspectives of radio propagationopportunities which may be considered in addressing correlation withmobile to base station ranging.

FIG. 2 shows aspects of the two-ray radio propagation model and theeffects of urban clutter.

FIG. 3 provides a typical example of how the statistical power budget iscalculated in design of a Commercial Mobile Radio Service Providernetwork.

FIG. 4 illustrates an overall view of a wireless radio location networkarchitecture, based on advanced intelligent network (AIN) principles.

FIG. 5 is a high level block diagram of an embodiment of the presentinvention for locating a mobile station (MS) within a radio coveragearea for the present invention.

FIG. 6 is a high level block diagram of the location center 142.

FIG. 7 is a high level block diagram of the hypothesis evaluator for thelocation center.

FIG. 8 is a substantially comprehensive high level block diagramillustrating data and control flows between the components of (and/oraccessed by) the location center/gateway 142, as well the functionalityof these components.

FIGS. 9A and 9B are a high level data structure diagram describing thefields of a location hypothesis object generated by the first ordermodels 1224 of the location center.

FIG. 10 is a graphical illustration of the computation performed by themost likelihood estimator 1344 of the hypothesis evaluator.

FIG. 11 is a high level block diagram of the mobile base station (MBS).

FIG. 12 is a high level state transition diagram describingcomputational states the Mobile Base station enters during operation.

FIG. 13 is a high level diagram illustrating the data structuralorganization of the Mobile Base station capability for autonomouslydetermining a most likely MBS location from a plurality of potentiallyconflicting MBS location estimating sources.

FIG. 14 illustrates the primary components of the signal processingsubsystem.

FIG. 15 illustrates how automatic provisioning of mobile stationinformation from multiple CMRS occurs.

FIG. 16 illustrates another embodiment of the location engine 139,wherein the context adjuster 1326 (denoted in this figure as “locationhypothesis adjuster modules”) includes a module (1436) that is capableof adjusting location hypotheses for reliability, and another module(1440) that is capable of adjusting location hypotheses for accuracy.

FIG. 17 illustrates the primary components of the signal processingsubsystem.

FIG. 18 is a block diagram further illustrating the present invention asa wireless location gateway.

FIG. 19 is a block diagram of an electronic networked yellow pages forproviding intelligent advertising services, wherein wireless locationservices may be utilized.

FIGS. 20A and 20B show a flowchart of the steps performed for routing auser along a route that includes a plurality of locations where the usercan access a desired item (product or service) at each of the pluralityof locations.

DETAILED DESCRIPTION

Detailed Description Introduction

When performing wireless location as described herein, substantialimprovements in radio location can be achieved since CDMA and otheradvanced radio communication infrastructures can be used for enhancingradio location. For example, the capabilities of IS-41 and advancedintelligent network (AIN) already provide a coarse-granularity ofwireless location, as is necessary to, for example, properly direct aterminating call to an MS. Such information, originally intended forcall processing usage, can be re-used in conjunction with the wirelesslocation processing described herein to provide wireless location in thelarge (i.e., to determine which country, state and city a particular MSis located), and wireless location in the small (i.e., which location,plus or minus a few hundred feet a given MS is located).

FIG. 4 is a high level diagram of one embodiment of a wirelessradiolocation architecture for the present invention. Accordingly, thisfigure illustrates the interconnections between the components of awireless cellular communication network, such as, a typical PCS networkconfiguration and various components that are specific to the presentinvention. In particular, as one skilled in the art will understand, atypical wireless (PCS) network includes:

-   -   (a) a (large) plurality of wireless mobile stations (MSs) 140        for at least one of voice related communication, visual (e.g.,        text such as is provided by a short message service) related        communication, and according to present invention, location        related communication. Note that some of the MSs 140 may include        the electronics and corresponding software to detect and process        signals from non-terrestrial transmission stations such as GPS        and/or GLONASS satellites. Moreover, note that such        non-terrestrial transmission stations can also be high attitude        aircraft which, e.g., can hover over a metropolitan area thereby        facilitating wireless communications;    -   (b) a mobile switching center (MSC) 112;    -   (c) a plurality of wireless cell sites in a radio coverage area        120, wherein each cell site includes an infrastructure base        station such as those labeled 122 (or variations thereof such as        122A–122D). In particular, the base stations 122 denote the        standard high traffic, fixed location base stations used for        voice and data communication with a plurality of MSs 140, and,        according to the present invention, also used for communication        of information related to locating such MSs 140. Additionally,        note that the base stations labeled 152 are more directly        related to wireless location enablement. For example, as        described in greater detail hereinbelow, the base stations 152        may be low cost, low functionality transponders that are used        primarily in communicating MS location related information to        the location center 142 (via base stations 122 and the MSC 112).        Note that unless stated otherwise, the base stations 152 will be        referred to hereinafter as location base station(s) 152 or        simply LBS(s) 152;    -   (d) a public switched telephone network (PSTN) 124 (which may        include signaling system links 106 having network control        components such as: a service control point (SCP) 104, one or        more signaling transfer points (STPs) 110.

In addition, the present invention provides one or more locationcenters/gateways 142. Such gateways may be described at a high level asfollows.

Location Center/Gateway 142 Description

A location center/gateway 142, (also be referred to as a locationcenter/gateway, or simply gateway), in response to a location requestreceived at the location center, can request activation of one or moreof a plurality of wireless location techniques in order to locate an MS140.

Various embodiments are provided herein of the location center/gateway142. In particular, FIG. 18 is block diagram illustrating anotherembodiment of the location center/gateway 142 of the present invention.Note that the wireless location gateway activation requests may bedependent upon, e.g.,

-   -   (a) a wireless network with which the MS 140 may be in contact,        such a network may be:        -   (i) a commercial mobile radio network supporting telephony            functionality,        -   (ii) a short messaging service or paging network;        -   (iii) a wireless network of beacons for providing location            related information such as GPS and LORAN C,        -   (iv) wireless carrier independent networks for performing            wireless location such as the wireless location network            provided by Times Three, Suite #220, Franklin Atrium, 3015            5th Avenue N.E, Calgary, AB T2A 6TB,        -   (v) a wireless broadcasting network for use in activating an            MS 140 of, e.g., a stolen vehicle such as is provided by            LoJack Corporation, 333 Elm Street, Dedham, Mass. 02026,            and/or        -   (vi) a hybrid network including portions of wireless            networks each network providing different types of signal            measurements for performing wireless location);    -   (b) the location signal measurement obtaining capabilities of        the wireless network with which the MS may be in contact. For        example, such a network may only support a network centric        location technique;    -   (c) the functionality of the MS 140 such as: the type(s) of        wireless signals which can be detected and processed by the MS        such as:        -   (i) non-terrestrial signals such as GPS signals,        -   (ii) signals from wireless beaconing/broadcasting systems            such as for LORAN C signals or stolen vehicle broadcast            networks for activating an MS 140 attached to the stolen            vehicle, or        -   (iii) wireless telephony protocols like CDMA, TDMA, and/or            GSM,    -   (d) a likely location of the target MS 140. For example, if the        target MS 140 is likely to be in Japan rather than the United        States, then the location service provider contacted by the        gateway 142 may be different from the location service provider        if the MS is likely to be in the U.S.

Moreover, regarding the plurality of wireless location techniques(embodiments thereof also denoted herein as “location estimators”) forwhich activation may be requested by the gateway, these techniques maybe co-located with the gateway, accessible via a network including: (i)local area networks, and (ii) wide area networks such as a telephony(wired or wireless) network, the Internet or a cable network. Thegateway 142 may supply to one or more of the location estimators,measurements of communications between the MS 140 and one or morenetworks for determining a location of the MS 140. Alternatively,instead of supplying such measurements (locally or remotely, and, via anetwork or otherwise), the gateway 142 may provide, with the locationactivation request, an identification of where the measurements may beobtained (e.g., one or more network addresses). In yet anotheralternative, such a gateway 142 may also send request(s) to thenetwork(s) having such MS communication measurements to forward them toparticular location estimators. Note, that in performing these tasks,the gateway 142 may receive with a location request (or may retrieve inresponse thereto) information regarding the functionality of the targetMS 140, e.g., as discussed above. Accordingly, such information may beused in selecting the location estimator to which an activation requestis provided. Thus, the gateway 142 may be the intermediary betweenlocation requesting applications and the location estimators, therebyproviding a simple, uniform application programming interface (API) forsuch applications substantially independently of the location estimatorsthat are activated to fulfill such location requests. Moreover, thegateway 142 (or embodiments thereof can substantially ease the burden ongeolocation service providers by providing a substantially uniformmethod for obtaining target MS/network signal data for use in locatingthe target MS. Thus, by interfacing to the gateway 142, a locationservice provider may substantially reduce the number and complexity ofits data exchange interfaces with the wireless networks for obtainingtarget MS/network signal data. Similarly, the networks capturing suchsignal data may also reduce the complexity and number of theirinterfaces for providing such signal data to location service providers.Additionally, note that the gateway may also fulfill location requestswherein the location is for a stationary and/or wireline handset insteadof a mobile station 140. Accordingly, the gateway 142 may request accessto, e.g., phone location information stored in a carrier's database ofpremise provisioning equipment as one skilled in the art willunderstand.

In some embodiments of the gateway 142, it may also facilitate in theproviding of certain location related services in addition to providing,e.g., MS 140 locations. In particular, one or more of the followinglocation related services may be facilitated by the gateway 142 or maybe made operative via the wireless location capabilities of the gateway142. However, note that the following location related services can, ingeneral, be provided without use of a gateway 142, albeit, e.g., in alikely more restricted context wherein not all available wirelesslocation estimating techniques are utilized, and/or by multiplying thenumber of interfaces to geolocation service providers (e.g., distinctwireless location interfaces provided directly to each wireless locationservice provider utilized). Further note that some of these applicationsare described in greater detail in later sections herein:

-   -   (10.1) Routing instructions for directing a vehicle or person to        get to a desired destination. Note, that there are various forms        of utilizing MS location capabilities to determine an        appropriate route, and related teachings are provided in        copending U.S. patent application titled, “Wireless Location        Using A Plurality of Commercial Network Infrastructures,”        by F. W. LeBlanc, Dupray and Karr filed Jan. 22, 1999 and having        U.S. Pat. No. 6,236,365 issued May 22, 2001 which is fully        incorporated herein by reference, and by the following two        copending U.S. patent applications which are also incorporated        herein by reference: (i) “Location Of A Mobile Station” filed        Nov. 24, 1999 having application Ser. No. 09/194,367 whose        inventors are Dupray and Karr, and (ii) “A Wireless Location        System For Calibrating Multiple Location Estimators” filed Oct.        21, 1998 having application Ser. No. 09/176,587 whose inventor        is Dupray. Additionally, other routing services (e.g., as        illustrated by the “routing services” component in FIG. 18) may        also be provided by the gateway 142 (or by service providers in        cooperation with the gateway). For example, the gateway 142 may        cooperate with an automated speech recognition interpretation        and synthesis unit for providing substantially automated        interactive communication with an MS 140 for providing spoken        directions. Note that such directions may be provided in terms        of street names and/or descriptions of the terrain (e.g., “the        glass high rise on the left having pink tinted glass”).    -   (10.2) Advertising may be directed to an MS 140 according to its        location. In at least some studies it appears that MS 140 users        do not respond well to unsolicited wireless advertisement        whether location based or otherwise. However, in response to        certain user queries for locally available merchandise, certain        advertisements may be viewed in a more friendly light. Thus, by        allowing an MS user to contact, e.g., a wireless advertising        portal by voice or via wireless Internet, and describe certain        merchandise desired (e.g., via interacting with an automated        speech interaction unit) the user may be able to describe and        receive (at his/her MS 140) visual displays of merchandise that        may satisfy such a users request. For example, an MS user may        provide a spoken request such as: “I need a shirt, who has        specials near here?”.    -   (10.3) Applications that combine routing with safety for        assisting MS users with requests such as “How do I get back to        the hotel safely?”;    -   (10.4) Applications that combine routing with sight seeing        guided tour where routing is interactive and depending on        feedback from users regarding, e.g., user interests;    -   (10.5) Applications using Internet picture capture with real        time voice capture and MS location (e.g., sightseeing, security,        and law enforcement),    -   (10.6) Intelligent transportation (e.g., voice commanded        vehicles)    -   (10.7) Applications that monitor whether or not a person or        object (e.g., a vehicle) is within a predetermined boundary.        Note, that such as an application may automatically provide        speech output to the MS user (or other authorized user) when the        person or object is beyond the predetermined boundary;    -   (10.8) Applications that route to an event and automatically        determine parking availability and where to park;    -   (10.9) Traffic/weather condition routing

Further note that various architectures for the location center/locationgateway are within the scope of the invention including a distributedarchitecture wherein in addition to the FOMs being possibly remotelyaccessed (e.g., via a communications network such as the Internet), thegateway itself may be distributed throughout one or more communicationnetworks. Thus, a location request received at a first location gatewayportion may be routed to a second location gateway portion (e.g., viathe Internet). Such a distributed gateway may be considered a“meta-gateway” and in fact such gateway portions may be fullyfunctioning gateways in their own right. Thus, such routing therebetweenmay be due to contractual arrangements between the two gateways (eachfulfilling location requests for a different network, wireless carrier,and/or geographical region). For example, for locating a stolen vehicle,it is not uncommon for the stolen vehicle to be transported rapidlybeyond the coverage area of a local or regional wireless vehiclelocating service. Moreover, a given location gateway may providelocation information for only certain areas corresponding, e.g., tocontractual arrangements with the wireless carriers with which thelocation gateway is affiliated. Thus, a first location gateway mayprovide vehicle locations for a first collection of one or more wirelessnetworks, and a second location gateway may provide vehicle locationsfor a second collection of one or more wireless networks. Accordingly,for an MS 140 built into a vehicle which can be detected by one or morewireless networks (or portions thereof in each of the first and secondcollections, then if the vehicle is stolen, the first gateway may beinitially contacted for determining whether the vehicle can be locatedvia communications with the first collection of one or more wirelessnetworks, and if the vehicle can not be located, the first gateway mayprovide a location request to the second gateway for thereby locatingthe stolen vehicle via wireless communications with one or more wirelessnetworks of the second collection. Furthermore, the first gateway mayprovide location requests for the stolen vehicle to other locationgateways.

The present invention provides the following additional components:

-   -   (11.1) one or more mobile base stations 148 (MBS) which are        optional, for physically traveling toward the target MS 140 or        tracking the target MS;    -   (11.2) a plurality of location base stations 152 (LBS) which are        optional, distributed within the radio coverage areas 120, each        LBS 152 having a relatively small MS 140 detection area 154.        Note that such LBSs 152 may also support Internet and/or TCP/IP        transmissions for transmitting visual location related        information (e.g., graphical, or pictorial) related to an MS        location request.

Since location base stations 152 can be located on, e.g., each floor ofa multi-story building, the wireless location technology describedherein can be used to perform location in terms of height as well as bylatitude and longitude.

In operation, an MS 140 may utilize one or more of the wirelesstechnologies, CDMA, TDMA, AMPS, NAMPS or GSM for wireless communicationwith: (a) one or more infrastructure base stations 122, (b) mobile basestation(s) 148, or (c) an LBS 152. Additionally, note that in someembodiments of the invention, there may be MS to MS communication.

Referring to FIG. 4 again, additional detail is provided of typical basestation coverage areas, sectorization, and high level components withina radio coverage area 120, including the MSC 112. Three exemplary basestations (BSs) are 122A, 122B and 122C, each of which radiatereferencing signals within their area of coverage 169 to facilitatemobile station (MS) 140 radio frequency connectivity, and various timingand synchronization functions. Note that some base stations may containno sectors 130 (e.g. 122E), thus radiating and receiving signals in a360 degree omnidirectional coverage area pattern, or the base stationmay contain “smart antennas” which have specialized coverage areapatterns. However, the generally most frequent base stations 122 havethree sector 130 coverage area patterns. For example, base station 122Aincludes sectors 130, additionally labeled a, b and c. Accordingly, eachof the sectors 130 radiate and receive signals in an approximate 120degree arc, from an overhead view. As one skilled in the art willunderstand, actual base station coverage areas 169 (stylisticallyrepresented by hexagons about the base stations 122) generally aredesigned to overlap to some extent, thus ensuring seamless coverage in ageographical area. Control electronics within each base station 122 areused to communicate with a mobile stations 140. Information regardingthe coverage area for each sector 130, such as its range, area, and“holes” or areas of no coverage (within the radio coverage area 120),may be known and used by the location center 142 to facilitate locationdetermination. Further, during communication with a mobile station 140,the identification of each base station 122 communicating with the MS140 as well, as any sector identification information, may be known andprovided to the location center 142.

In the case of the base station types 122, 148, and 152 communicatinglocation information, a base station or mobility controller 174 (BSC)controls, processes and provides an interface between originating andterminating telephone calls from/to mobile station (MS) 140, and themobile switch center (MSC) 112. The MSC 122, on-the-other-hand, performsvarious administration functions such as mobile station 140registration, authentication and the relaying of various systemparameters, as one skilled in the art will understand.

The base stations 122 may be coupled by various transport facilities 176such as leased lines, frame relay, T-Carrier links, optical fiber linksor by microwave communication links.

When an MS 140 is powered on and in the idle state, it constantlymonitors the pilot signal transmissions from each of the base stations122 located at nearby cell sites. Since base station/sector coverageareas may often overlap, such overlapping enables an MS 140 to detect,and, in the case of certain wireless technologies, communicatesimultaneously along both the forward and reverse paths, with multiplebase stations 122 and/or sectors 130. In FIG. 4, the constantlyradiating pilot signals from base station sectors 130, such as sectorsa, b and c of BS 122A, are detectable by MSs 140 within the coveragearea 169 for BS 122A. That is, the mobile stations 140 scan for pilotchannels, corresponding to a given base station/sector identifiers(IDs), for determining in which coverage area 169 (i.e., cell) it iscontained. This is performed by comparing signal strengths of pilotsignals transmitted from these particular cell-sites.

The mobile station 140 then initiates a registration request with theMSC 112, via the base station controller 174. The MSC 112 determineswhether or not the mobile station 140 is allowed to proceed with theregistration process (except, e.g., in the case of a 911 call, whereinno registration process is required). Once any required registration iscomplete, calls may be originated from the mobile station 140 or callsor short message service messages can be received from the network. Notethat the MSC 112 communicates as appropriate, with a class 4/5 wirelinetelephony circuit switch or other central offices, connected to the PSTN124 network. Such central offices connect to wireline terminals, such astelephones, or any communication device compatible with a wireline. ThePSTN 124 may also provide connections to long distance networks andother networks.

The MSC 112 may also utilize IS/41 data circuits or trunks connecting tosignal transfer point 110, which in turn connects to a service controlpoint 104, via Signaling System #7 (SS7) signaling links (e.g., trunks)for intelligent call processing, as one skilled in the art willunderstand. In the case of wireless AIN services such links are used forcall routing instructions of calls interacting with the MSC 112 or anyswitch capable of providing service switching point functions, and thepublic switched telephone network (PSTN) 124, with possible terminationback to the wireless network.

Referring still to FIG. 4, the location center/gateway (LC) 142interfaces with the MSC 112 either via dedicated transport facilities178, using, e.g., any number of LAN/WAN technologies, such as Ethernet,fast Ethernet, frame relay, virtual private networks, etc., or via thePSTN 124. The gateway 142 may receive autonomous (e.g., unsolicited)command/response messages regarding, for example: (a) the state of thewireless network of each commercial radio service provider utilizing theLC 142 for wireless location services, (b) MS 140 and BS 122 radiofrequency (RF) measurements, (c) communications with any MBSs 148, and(d) location applications requesting MS locations using the locationcenter/gateway 142. Conversely, the LC 142 may provide data and controlinformation to each of the above components in (a)–(d). Additionally,the LC 142 may provide location information to an MS 140, via a BS 122.Moreover, in the case of the use of a mobile base station (MBS) 148,several communications paths may exist with the LC 142.

The MBS 148 may act as a low cost, partially-functional, moving basestation, and is, in one embodiment, situated in a vehicle (e.g., land,water or aircraft) where an operator may engage in MS 140 searching andtracking activities. In providing these activities using CDMA, the MBS148 provides a forward link pilot channel for a target MS 140, andsubsequently receives unique BS pilot strength measurements from the MS140. The MBS 148 also includes a mobile station 140 for datacommunication with the gateway 142, via a BS 122. In particular, suchdata communication includes telemetering at least the geographicposition (or estimates thereof) of the MBS 148, various RF measurementsrelated to signals received from the target MS 140, and in someembodiments, MBS 148 estimates of the location of the target MS 140. Insome embodiments, the MBS 148 may utilize multiple-beam fixed antennaarray elements and/or a moveable narrow beam antenna, such as amicrowave dish 182. The antennas for such embodiments may have a knownorientation in order to further deduce a radio location of the target MS140 with respect to an estimated current location of the MBS 148. Aswill be described in more detail herein below, the MBS 148 may furthercontain a satellite (e.g., global positioning system (GPS)) receiver (orother receiver for non-terrestrial wireless signals) for determining thelocation of the MBS 148 and/or providing wireless location assistance atarget MS 140, e.g., providing GPS information to the MS to assist theMS in determining its location. Additionally, the MBS 148 may includedistance sensors, dead-reckoning electronics, as well as an on-boardcomputing system and display devices for locating both the MBS 148itself as well as tracking and locating the target MS 140. The computingand display provides a means for communicating the position of thetarget MS 140 on a map display to an operator of the MBS 148. It isimportant to note that in one embodiment, an MBS 148 may determine itslocation substantially independent of the communications network(s) withwhich the MBS communicates.

Each location base station (LBS) 152 is a low cost location device. Insome embodiments, to provide such LBS's cost effectively, each LBS 152only partially or minimally supports the air-interface standards of theone or more wireless technologies used in communicating with both theBSs 122 and the MSs 140. Each LBS 152, when put in service, is placed ata fixed location, such as at a traffic signal, lamp post, etc., whereinthe location of the LBS may be determined as accurately as, for example,the accuracy of the locations of the infrastructure BSs 122. Assumingthe wireless technology, CDMA, is used, each BS 122 uses a time offsetof the pilot PN sequence to identify a forward CDMA pilot channel. Inone embodiment, each LBS 152 emits a unique, time-offset pilot PNsequence channel in accordance with the CDMA standard in the RF spectrumdesignated for BSs 122, such that the channel does not interfere withneighboring BSs 122 cell site channels, and does not interfere withneighboring LBSs 152. Each LBS 152 may also contain multiple wirelessreceivers in order to monitor transmissions from a target MS 140.Additionally, each LBS 152 contains mobile station 140 electronics,thereby allowing the LBS to both be controlled by, e.g., the gateway 142or the wireless carrier(s) for the LBS, and to transmit information to,e.g., the gateway 142 (via, e.g., at least one neighboring BS 122), orto another wireless location service provider such as one providing oneor more FOMs.

As mentioned above, when the location of a particular target MS 140 isdesired, the gateway 142 may request location information about thetarget MS 140 from, for instance, one or more activated LBSs 152 in ageographical area of interest. Accordingly, whenever the target MS 140is in an LBS coverage area, or is suspected of being in the coveragearea, either upon command from the gateway 142 (or other locationservice provider), or in a substantially continuous (or periodic)fashion, the LBS's pilot channel appears to the target MS 140 as apotential neighboring base station channel, and consequently, is placed,for example, in the CDMA neighboring set, or the CDMA remaining set ofthe target MS 140 (as one familiar with the CDMA standards willunderstand).

During the normal CDMA pilot search sequence of the mobile stationinitialization state (in the target MS), the target MS 140 will, ifwithin range of such an activated LBS 152, detect the LBS pilot presenceduring the CDMA pilot channel acquisition substate. Consequently, thetarget MS 140 performs RF measurements on the signal from each detectedLBS 152. Similarly, an activated LBS 152 can perform RF measurements onthe wireless signals from the target MS 140. Accordingly, each LBS 152detecting the target MS 140 may subsequently telemeter back to the LC142 measurement results related to signals from/to the target MS 140.Moreover, upon command, the target MS 140 may telemeter back to thegateway 142 its own measurements of the detected LBSs 152, andconsequently, this new location information, in conjunction withlocation related information received from the BSs 122, can be used tolocate the target MS 140.

It should be noted that an LBS 152 will normally deny hand-off requests,since typically the LBS does not require the added complexity ofhandling voice or traffic bearer channels, although economics and peaktraffic load conditions may dictate preference here. Note that GPStiming information, needed by any CDMA base station, is either achievedvia a the inclusion of a local GPS receiver or via a telemetry processfrom a neighboring conventional BS 122, which contains a GPS receiverand timing information. Since energy requirements are minimal in such anLBS 152, (rechargeable) batteries or solar cells may be used to powerthe LBSs. Further, no expensive terrestrial transport link is typicallyrequired since two-way communication is provided by an included MS 140(or an electronic variation thereof) within each LBS. Thus, LBSs 152 maybe placed in numerous locations, such as:

-   -   (a) in dense urban canyon areas (e.g., where signal reception        may be poor and/or very noisy);    -   (b) in remote areas (e.g., hiking, camping and skiing areas);    -   (c) along highways (e.g., for emergency as well as monitoring        traffic flow), and their rest stations; or    -   (d) in general, wherever more location precision is required        than is obtainable using other wireless infrastructure network        components.        Location Center—Network Elements API Description

A location application programming interface 136 (FIG. 4), denotedL-API, is may be provided between the location center/gateway 142 (LC)and the mobile switch center (MSC) network element type, in order tosend and receive various control, signals and data messages. The L-APImay be implemented using a preferably high-capacity physical layercommunications interface, such as IEEE standard 802.3 (10 baseTEthernet), although other physical layer interfaces could be used, suchas fiber optic ATM, frame relay, etc. At least two forms of L-APIimplementation are possible. In a first case, the signal control anddata messages are provided using the MSC 112 vendor's native operationsmessages inherent in the product offering, without any specialmodifications. In a second case, the L-API includes a full suite ofcommands and messaging content specifically optimized for wirelesslocation purposes, which may require some, although minor development onthe part of an MSC vendor.

Signal Processor Description

Referring to FIG. 17, a signal processing subsystem (labeled 1220 inother figures) may be provided (or accessed) by the gateway 142. Such asignal processing subsystem may: (a) receive control messages and signalmeasurements from one or more wireless service provider networks, and(b) transmit appropriate control messages to such wireless networks viathe location applications programming interface 136 referenced earlier,for wireless location purposes. The signal processing subsystem 1220additionally provides various signal identification, conditioning andpre-processing functions, including buffering, signal typeclassification, signal filtering, message control and routing functionsto the location estimating modules or FOMs.

There can be several combinations of Delay Spread/Signal Strength setsof measurements made available to the signal processing subsystem 1220.In some cases a mobile station 140 (FIG. 1) may be able to detect up tothree or four pilot channels representing three to four base stations,or as few as one pilot channel, depending upon the environment andwireless network configuration. Similarly, possibly more than one BS 122can detect a mobile station 140 transmitter signal, and the fact thatmultiple CMRS' base station equipment commonly will overlap coverageareas.

For each mobile station 140 or BS 122 transmitted signal that isdetected by a receiver group at a base or mobile station, respectively,multiple delayed signals, or “fingers” may be detected (e.g., in CDMA)and tracked resulting from multipath radio propagation conditions from agiven transmitter. In typical spread spectrum diversity CDMA receiverdesign, the “first” finger represents the most direct, or least delayedmultipath signal. Second or possibly third or fourth fingers may also bedetected and tracked, assuming the detecting base station and/or mobilestation 140 contains a sufficient number of data receivers for doing so.The signal processing subsystem may utilize various wireless signalmeasurements of transmissions between a target mobile station 140 and anetwork of base stations 122, 152 and/or 148. Such measurements can beimportant in effectively estimating the location of mobile stations 140in that it is well known that measurements of wireless signalpropagation characteristics, such as signal strength (e.g., RSSI), timedelay, angle of arrival, and any number other measurements, canindividually lead to gross errors in MS 140 location estimates.

Accordingly, one aspect of the present invention is directed toutilizing a larger number of wireless signal measurements, and utilizinga plurality of MS 140 estimation techniques to compensate for locationestimation errors generated by some such techniques. For example, due tothe large capital outlay costs associated with providing three or moreoverlapping base station coverage signals in every possible location,most practical digital PCS deployments result in fewer than three basestation pilot channels being reportable in the majority of locationareas, thus resulting in a larger, more amorphous location estimates byterrestrial triangulation systems. Thus, by utilizing wireless signalmeasurements from a variety of sources substantially simultaneouslyand/or “greedily” (i.e., use whatever signal measurements can beobtained from any of the signal sources as they are obtained),additional location enhancements can be obtained. For example, byenhancing a mobile station 140 with electronics for detecting satellitetransmissions (as done with mobile base stations 148 and which also canbe viewed as such an enhanced mobile station 140) additional locationrelated signals maybe obtained from:

-   -   (a) the GPS satellite system,    -   (b) the Global Navigation Satellite System (GLONASS) satellite        system, a Russian counterpart to the U.S. GPS system, and/or    -   (c) the numerous low earth orbit satellite systems (LEOs) and        medium earth orbit satellite systems (MEOs) such as the IRIDIUM        system being developed by Motorola Corp., the GLOBALSTAR system        by Loral and Qualcomm, and the ICO satellite system by ICO        Global Communications.        Thus, by combining even insufficient wireless location        measurements from different wireless communication systems,        accurate location of an MS 140 is possible. For example, by if        only two GPS satellites are detectable, but there is an        additional reliable wireless signal measurement from, e.g., a        terrestrial base station 122, then by triangulating using        wireless signal measurements derived from transmissions from        each of these three sources, a potentially reliable and accurate        MS location can be obtained.

Moreover, the transmissions from the MS 140 used for determining theMS's location need not be transmitted to terrestrial base stations(e.g., 122). It is within the scope of the present invention that atarget MS 140 may transmit location related information to satellites aswell. For example, if a target MS 140 detects two GPS satellitetransmissions and is able to subsequently transmit the GPS signalmeasurements (e.g., timing measurements) to an additional satellitecapable of determining additional MS location measurements according tothe signals received, then by performing a triangulation process at thelocation center/gateway 142 (which may be co-located with the additionalsatellite, or at a remote terrestrial site), a potentially reliable andaccurate MS location can be obtained. Accordingly, the present inventionis capable of resolving wireless location ambiguities due to a lack oflocation related information of one type by utilizing supplementallocation related information of a different type. Note that by “type” asused here it is intended to be interpreted broadly as, e.g.,

-   -   (a) a data type of location information, and/or    -   (b) communications from a particular commercial wireless system        as opposed to an alternative system, each such system having        distinct groups of known or registered MS users.

Moreover, it can be that different FOMs are provided for at least somewireless location computational models utilizing different types oflocation related information. For example, in certain contexts wirelessnetworks based on different wireless signaling technologies may be usedto locate an MS 140 during the time period of a single emergency callsuch as E911. Moreover, in other contexts it may be possible for thetarget MS 140 to use one or more of a plurality of wirelesscommunication networks, possibly based on different wirelesscommunication technologies, depending on availability the of technologyin the coverage area. In particular, since so called “dual mode” or“tri-mode” mobile stations 140 are available, wherein such mobilestations are capable of wireless communication in a plurality ofwireless communication technologies, such as digital (e.g., CDMA, and/orTDMA) as well as analog or AMP/NAMPS, such mobile stations may utilize afirst (likely a default) wireless communication technology wheneverpossible, but switch to another wireless communication technology when,e.g., coverage of the first wireless technology becomes poor. Moreover,such different technologies are typically provided by different wirelessnetworks (wherein the term “network” is understood to include a networkof communication supporting nodes geographically spaced apart thatprovide a communications infrastructure having access to informationregarding subscribers to the network prior to a request to access thenetwork by the subscribers). Accordingly, the present invention mayinclude (or access) FOMs for providing mobile station location estimateswherein the target MS 140 communicates with various networks usingdifferent wireless communication technologies. Moreover, such FOMs maybe activated according to the wireless signal measurements received fromvarious wireless networks and/or wireless technologies supported by atarget MS 140 and to which there is a capability of communicatingmeasurements of such varied wireless signals to the FOM(s). Thus, in oneembodiment of the present invention, there may be a triangulation (ortrilateration) based FOM for each of CDMA, TDMA and AMP/NAMPS which maybe singly, serially, or concurrently for obtaining a particular locationof an MS 140 at a particular time (e.g., for an E911 call). Thus, whenlocating a target MS 140, the MS may, if there is overlapping coverageof two wireless communication technologies and the MS supportscommunications with both, repeatedly switch back and forth between thetwo thereby providing additional wireless signal measurements for use inlocating the target MS 140.

In one embodiment of the present invention, wherein multiple FOMs may beactivated substantially simultaneously (or alternatively, whereverappropriate input is received that allow particular FOMs to beactivated). Note that at least some of the FOMs may provide “inverse”estimates of where a target MS 140 is not instead of where it is. Suchinverse analysis can be very useful in combination with locationestimates indicating where the target MS is in that the accuracy of aresulting MS location estimate may be substantially decreased in sizewhen such inverse estimates are utilized to rule out areas thatotherwise appear to be likely possibilities for containing the target MS140. Note that one embodiment of a FOM that can provide such reverseanalysis is a location computational model that generates target MSlocation estimates based on archived knowledge of base station coverageareas (such an archive being the result of, e.g., the compilation a RFcoverage database—either via RF coverage area simulations or fieldtests). In particular, such a model may provide target MS locationinverse estimates having a high confidence or likelihood that that thetarget MS 140 is not in an area since either a base station 122 (or 152)can not detect the target MS 140, or the target MS can not detect aparticular base station. Accordingly, the confidences or likelihoods onsuch estimates may be used by diminishing a likelihood that the targetMS is in an area for the estimate, or alternatively the confidence orlikelihood of all areas of interest outside of the estimate canincreased.

Note that in some embodiments of the present invention, bothmeasurements of forward wireless signals to a target MS 140, andmeasurements of reverse wireless signals transmitted from the target MSto a base station can be utilized by various FOMs. In some embodiments,the received relative signal strength (RRSS_(BS)) of detected nearbybase station transmitter signals along the forward link to the targetmobile station can be more readily used by the location estimate modules(FOMs) since the transmission power of the base stations 122 typicallychanges little during a communication with a mobile station. However,the relative signal strength (RRSS_(MS)) of target mobile stationtransmissions received by the base stations on the reverse link mayrequire more adjustment prior to location estimate model use, since themobile station transmitter power level changes nearly continuously.

Location Center High Level Functionality

At a very high level the location center/gateway 142 computes (orrequests computation of) location estimates for a wireless mobilestation 140 by performing at least some of the following steps:

(23.0) receiving an MS location request;

(23.1) receiving measurements of signal transmission characteristics ofcommunications communicated between the target MS 140 and one or morewireless infrastructure base stations 122. Note, this step may only beperformed if the gateway provides such measurements to a FOM (e.g., aFOM co-located therewith);(23.2) filtering the received signal transmission characteristics (by asignal processing subsystem 1220 illustrated in, e.g., FIGS. 5 and 30)as needed so that target MS location data can be generated that isuniform and consistent with location data generated from other targetMSs 140. In particular, such uniformity and consistency is both in termsof data structures and interpretation of signal characteristic valuesprovided by the MS location data, as will be described hereinbelow.Note, this step may also only be performed if the gateway provides suchmeasurements to a FOM. Otherwise, such FOM is likely to perform suchfiltering;(23.3) inputting the generated target MS location data to one or more MSlocation estimating models (FOMs, labeled collectively as 1224 in FIG.5), so that each such FOM may use the input target MS location data forgenerating a “location hypothesis” providing an estimate of the locationof the target MS 140. Note, this step may also only be performed if thegateway provides such measurements to a FOM;(23.4) receiving the resulting location hypotheses from the activatedFOMs, and providing the generated location hypotheses to an hypothesisevaluation module (denoted the hypothesis evaluator 1228 in FIG. 5) for:

(a) (optionally) adjusting the target MS location estimates of thegenerated location hypotheses and/or adjusting confidence values of thelocation hypotheses, wherein for each location hypothesis, itsconfidence value indicates the confidence or likelihood that the targetMS is located in the location estimate of the location hypothesis.Moreover, note that such adjusting uses archival information related tothe accuracy and/or reliability of previously generated locationhypotheses;

(b) (optionally) evaluating the location hypotheses according to variousheuristics related to, for example, the radio coverage area 120 terrain,the laws of physics, characteristics of likely movement of the target MS140; and

(c) (necessarily) determining a most likely location area for the targetMS 140, wherein the measurement of confidence associated with each inputMS location area estimate may be used for determining a “most likelylocation area”; and

(23.5) outputting a most likely target MS location estimate to one ormore applications 146 (FIG. 5) requesting an estimate of the location ofthe target MS 140.

Location Hypothesis Data Representation

In order to describe how the steps (23.1) through (23.5) are performedin the sections below, some introductory remarks related to the datadenoted above as location hypotheses will be helpful. Additionally, itwill also be helpful to provide introductory remarks related tohistorical location data and the data base management programsassociated therewith.

For each target MS location estimate generated and utilized by thepresent invention, the location estimate is provided in a data structure(or object class) denoted as a “location hypothesis” (illustrated inTable LH-1). Brief descriptions of the data fields for a locationhypothesis is provided in the Table LH-1.

TABLE LH-1 FOM_ID First order model ID (providing this LoactionHypothesis); note, since it is possible for location hypotheses to begenerated by other than the FOMs 1224, in general, this field identifiesthe module that generated this location hypothesis. MS_ID Theidentification of the target MS 140 to this location hypothesis applies.pt_est The most likely location point estimate of the targets MS 140.valid_pt Boolean indicating the validity of “pt_est”. area_est LocationArea Estimate of the targets MS 140 provided by the FOM. This areaestimate will be used whenever “image_area” below is NULL. valid_areaBoolean indicating the validity of “area_est” (one of “pt_est” and“area_est” must be valid). adjust Boolean (true if adjustments to thefields of this location hypothesis are to be performed in the Contextadjuster Module). pt_covering Reference to a substantially minimal area(e.g., mesh cell) covering of “pt_est”. Note, since this MS 140 may besubstantially on a cell boundary, this covering may, in some cases, in-clude more than one cell. image_area Reference to a substantiallyminimal area (e.g., mesh cell) covering of “pt_covering” (see detaileddescription of the function, “confidence_adjuster”). Note that if thisfield is not NULL, then this is the target MS location estimate used bythe location center 142 instead of “area_est”. extrapolation_areaReference to (if non-NULL) an extrapolated MS target estimate areaprovided by the location extrapolator submodule 1432 of the hypothesisanalyzer 1332. That is, this field, if non-NULL, is an extrapolation ofthe “image_area” field if it exists, otherwise this field is anextrapolation of the “area_est” field. Note other extrapolation fieldsmay also be provided depend- ing on the embodiment of the presentinvention, such as an extrapolation of the “pt_covering”. confidence Inone embodiment, this is a probability indicat- ing a likelihood that thetarget MS 140 is in (or out) of a particular area. If “image_area”exists, then this is a measure of the likelihood that the target MS 140is within the area represented by “image_area”, or if “image_area” hasnot been computed (e.g., “adjust” is FALSE), then “area_est” must bevalid and this is a measure of the likelihood that the target MS 140 iswithin the area represented by “area_est”. Other embodiments, are alsowithin the scope of the present invention that are not probabilities;e.g., translations and/or expansions of the [0, 1] probability range asone skilled in the art will understand. Original_Timestamp Date and timethat the location signature cluster (defined hereinbelow) for thislocation hypothesis was received by the signal processing subsystem1220. Active_Timestamp Run-time field providing the time to which thislocation hypothesis has had its MS location esti- mate(s) extrapolated(in the location extrapolator 1432 of the hypothesis analyzer 1332).Note that this field is initialized with the value from the“Original_Timestamp” field. Processing Tags and For indicatingparticular types of environmental environmental classifications notreadily determined by the categorizations “Original_Timestamp” field(e.g., weather, traffic), and restrictions on location hypothesisprocesing. loc_sig_cluster Provides access to the collection of locationsignature signal characteristics derived from communications between thetarget MS 140 and the base station(s) detected by this MS (dis- cussedin detail hereinbelow); in particular, the location data accessed hereis provided to the first order models by the signal processing subsystem1220; i.e., access to the “loc sigs” (received at “timestamp” regardingthe location of the target MS) descriptor Original descriptor (from theFirst order model in- dicating why/how the Location Area Estimate andConfidence Value were determined).

As can be seen in the Table LH-1, each location hypothesis datastructure includes at least one measurement, denoted hereinafter as aconfidence value (or simply confidence); that is a measurement of theperceived likelihood that an MS location estimate in the locationhypothesis is an accurate location estimate of the target MS 140. Since,in some embodiments of the invention, such confidence values are animportant aspect, much of the description and use of such confidencevalues are described below; however, a brief description is providedhere.

In one embodiment, each confidence value is a probability indicative ofa likeliness that the target MS 140 resides within an geographic arearepresented by the hypothesis to which the confidence value applies.Accordingly, each such confidence value is in the range [0, 1].Moreover, for clarity of discussion, it is assumed that unless statedotherwise that the probabilistic definition provided here is to be usedwhen confidence values are discussed.

Note, however, other definitions of confidence values are within thescope of the present invention that may be more general thanprobabilities, and/or that have different ranges other than [0, 1]. Forexample, one such alternative is that each such confidence value is inthe range −1.0 to 1.0, wherein the larger the value, the greater theperceived likelihood that the target MS 140 is in (or at) acorresponding MS location estimate of the location hypothesis to whichthe confidence value applies. As an aside, note that a locationhypothesis may have more than one MS location estimate (as will bediscussed in detail below) and the confidence value will typically onlycorrespond or apply to one of the MS location estimates in the locationhypothesis. Further, values for the confidence value field may beinterpreted as: (a) −1.0 means that the target MS 140 is NOT in such acorresponding MS area estimate of the location hypothesis area, (b) 0means that it is unknown as to the likelihood of whether the MS 140 inthe corresponding MS area estimate, and (c)+1.0 means that the MS 140 isperceived to positively be in the corresponding MS area estimate.

Additionally, in utilizing location hypotheses in, for example, thelocation evaluator 1228 as in (23.4) above, it is important to keep inmind that for confidences, cf₁ and cf₂, if cf₁<=cf₂, then for a locationhypotheses H₁ and H₂ having cf₁ and cf₂, respectively, the target MS 140is expected to more likely reside in a target MS estimate of H₂ than atarget MS estimate of H₁. Moreover, if an area, A, is such that it isincluded in a plurality of location hypothesis target MS estimates, thena confidence score, CS_(A), can be assigned to A, wherein the confidencescore for such an area is a function of the confidences for all thelocation hypotheses whose (most pertinent) target MS location estimatescontain A. That is, in order to determine a most likely target MSlocation area estimate for outputting from the location center/gateway142, a confidence score is determined for areas within the locationcenter/gateway service area.

Coverage Area: Area Types and Their Determination

The notion of “area type” as related to wireless signal transmissioncharacteristics has been used in many investigations of radio signaltransmission characteristics. Some investigators, when investigatingsuch signal characteristics of areas have used somewhat naive areaclassifications such as urban, suburban, rural, etc. However, it isdesirable for the purposes of the present invention to have a moreoperational definition of area types that is more closely associatedwith wireless signal transmission behaviors.

To describe embodiments of the an area type scheme that may be used inthe present invention, some introductory remarks are first provided.Note that the wireless signal transmission behavior for an area dependson at least the following criteria:

-   -   (23.8.1) substantially invariant terrain characteristics (both        natural and man-made) of the area; e.g., mountains, buildings,        lakes, highways, bridges, building density;    -   (23.8.2) time varying environmental characteristics (both        natural and man-made) of the area; e.g., foliage, traffic,        weather, special events such as baseball games;    -   (23.8.3) wireless communication components or infrastructure in        the area; e.g., the arrangement and signal communication        characteristics of the base stations 122 in the area (e.g., base        station antenna downtilt). Further, the antenna characteristics        at the base stations 122 may be important criteria.

Accordingly, a description of wireless signal characteristics fordetermining area types could potentially include a characterization ofwireless signaling attributes as they relate to each of the abovecriteria. Thus, an area type might be: hilly, treed, suburban, having nobuildings above 50 feet, with base stations spaced apart by two miles.However, a categorization of area types is desired that is both moreclosely tied to the wireless signaling characteristics of the area, andis capable of being computed substantially automatically and repeatedlyover time. Moreover, for a wireless location system, the primarywireless signaling characteristics for categorizing areas into at leastminimally similar area types are: thermal noise and, more importantly,multipath characteristics (e.g., multipath fade and time delay).

Focusing for the moment on the multipath characteristics, it is believedthat (23.8.1) and (23.8.3) immediately above are, in general, moreimportant criteria for accurately locating an MS 140 than (23.8.2). Thatis, regarding (23.8.1), multipath tends to increase as the density ofnearby vertical area changes increases. For example, multipath isparticularly problematic where there is a high density of high risebuildings and/or where there are closely spaced geographic undulations.In both cases, the amount of change in vertical area per unit of area ina horizontal plane (for some horizontal reference plane) may be high.Regarding (23.8.3), the greater the density of base stations 122, theless problematic multipath may become in locating an MS 140. Moreover,the arrangement of the base stations 122 in the radio coverage area 120in FIG. 4 may affect the amount and severity of multipath.

Accordingly, it would be desirable to have a method and system forstraightforwardly determining area type classifications related tomultipath, and in particular, multipath due to (23.8.1) and (23.8.3).The present invention provides such a determination by utilizing a novelnotion of area type, hereinafter denoted “transmission area type” (or,“area type” when both a generic area type classification scheme and thetransmission area type discussed hereinafter are intended) forclassifying “similar” areas, wherein each transmission area type classor category is intended to describe an area having at least minimallysimilar wireless signal transmission characteristics. That is, the noveltransmission area type scheme of the present invention is based on: (a)the terrain area classifications; e.g., the terrain of an areasurrounding a target MS 140, (b) the configuration of base stations 122in the radio coverage area 120, and (c) characterizations of thewireless signal transmission paths between a target MS 140 location andthe base stations 122.

In one embodiment of a method and system for determining such(transmission) area type approximations, a partition (denotedhereinafter as P₀) is imposed upon the radio coverage area 120 forpartitioning for radio coverage area into subareas, wherein each subareais an estimate of an area having included MS 140 locations that arelikely to have is at least a minimal amount of similarity in theirwireless signaling characteristics. To obtain the partition P₀ of theradio coverage area 120, the following steps are performed:

-   -   (23.8.4.1) Partition the radio coverage area 120 into subareas,        wherein in each subarea is: (a) connected, (b) the subarea is        not too oblong, e.g., the variations in the lengths of chords        sectioning the subarea through the centroid of the subarea are        below a predetermined threshold, (c) the size of the subarea is        below a predetermined value, and (d) for most locations (e.g.,        within a first or second deviation) within the subarea whose        wireless signaling characteristics have been verified, it is        likely (e.g., within a first or second deviation) that an MS 140        at one of these locations will detect (forward transmission        path) and/or will be detected (reverse transmission path) by a        same collection of base stations 122. For example, in a CDMA        context, a first such collection may be (for the forward        transmission path) the active set of base stations 122, or, the        union of the active and candidate sets, or, the union of the        active, candidate and/or remaining sets of base stations 122        detected by “most” MSs 140 in. Additionally (or alternatively),        a second such collection may be the base stations 122 that are        expected to detect MSs 140 at locations within the subarea. Of        course, the union or intersection of the first and second        collections is also within the scope of the present invention        for partitioning the radio coverage area 120 according to (d)        above. It is worth noting that it is believed that base station        122 power levels will be substantially constant. However, even        if this is not the case, one or more collections for (d) above        may be determined empirically and/or by computationally        simulating the power output of each base station 122 at a        predetermined level. Moreover, it is also worth mentioning that        this step is relatively straightforward to implement using the        data stored in the location signature data base 1320 (i.e., the        verified location signature clusters discussed in detail        hereinbelow). Denote the resulting partition here as P₁.    -   (23.8.4.2) Partition the radio coverage area 120 into subareas,        wherein each subarea appears to have substantially homogeneous        terrain characteristics. Note, this may be performed        periodically substantially automatically by scanning radio        coverage area images obtained from aerial or satellite imaging.        For example, EarthWatch Inc. of Longmont, Colo. can provide        geographic with 3 meter resolution from satellite imaging data.        Denote the resulting partition here as P₂.    -   (23.8.4.3) Overlay both of the above partitions, P₁ and P₂ of        the radio coverage area 120 to obtain new subareas that are        intersections of the subareas from each of the above partitions.        This new partition is P₀ (i.e., P₀=P₁ intersect P₂), and the        subareas of it are denoted as “P₀ subareas”.

Now assuming P₀ has been obtained, the subareas of P₀ are provided witha first classification or categorization as follows:

-   -   (23.8.4.4) Determine an area type categorization scheme for the        subareas of P₁. For example, a subarea, A, of P₁, may be        categorized or labeled according to the number of base stations        122 in each of the collections used in (23.8.4.1)(d) above for        determining subareas of P₁. Thus, in one such categorization        scheme, each category may correspond to a single number x (such        as 3), wherein for a subarea, A, of this category, there is a        group of x (e.g., three) base stations 122 that are expected to        be detected by a most target MSs 140 in the area A. Other        embodiments are also possible, such as a categorization scheme        wherein each category may correspond to a triple: of numbers        such as (5, 2, 1), wherein for a subarea A of this category,        there is a common group of 5 base stations 122 with two-way        signal detection expected with most locations (e.g., within a        first or second deviation) within A, there are 2 base stations        that are expected to be detected by a target MS 140 in A but        these base stations can not detect the target MS, and there is        one base station 122 that is expected to be able to detect a        target MS in A but not be detected.    -   (23.8.4.5) Determine an area type categorization scheme for the        subareas of P₂. Note that the subareas of P₂ may be categorized        according to their similarities. In one embodiment, such        categories may be somewhat similar to the naive area types        mentioned above (e.g., dense urban, urban, suburban, rural,        mountain, etc.). However, it is also an aspect of the present        invention that more precise categorizations may be used, such as        a category for all areas having between 20,000 and 30,000 square        feet of vertical area change per 11,000 square feet of        horizontal area and also having a high traffic volume (such a        category likely corresponding to a “moderately dense urban” area        type).    -   (23.8.4.6) Categorize subareas of P₀ with a categorization        scheme denoted the “P₀ categorization,” wherein for each P₀        subarea, A, a “P₀ area type” is determined for A according to        the following substep(s):        -   (a) Categorize A by the two categories from (23.8.4.4) and            (23.8.5) with which it is identified. Thus, A is categorized            (in a corresponding P₀ area type) both according to its            terrain and the base station infrastructure configuration in            the radio coverage area 120.    -   (23.8.4.7) For each P₀ subarea, A, of P₀ perform the following        step(s):        -   (a) Determine a centroid, C(A), for A;        -   (b) Determine an approximation to a wireless transmission            path between C(A) and each base station 122 of a            predetermined group of base stations expected to be in (one            and/or two-way) signal communication with most target MS 140            locations in A. For example, one such approximation is a            straight line between C(A) and each of the base stations 122            in the group. However, other such approximations are within            the scope of the present invention, such as, a generally            triangular shaped area as the transmission path, wherein a            first vertex of this area is at the corresponding base            station for the transmission path, and the sides of the            generally triangular shaped defining the first vertex have a            smallest angle between them that allows A to be completely            between these sides.        -   (c) For each base station 122, BS_(i), in the group            mentioned in (b) above, create an empty list, BS_(i)-list,            and put on this list at least the P₀ area types for the            “significant” P₀ subareas crossed by the transmission path            between C(A) and BS_(i). Note that “significant” P₀ subareas            may be defined as, for example, the P₀ subareas through            which at least a minimal length of the transmission path            traverses. Alternatively, such “significant” P₀ subareas may            be defined as those P₀ subareas that additionally are know            or expected to generate substantial multipath.        -   (d) Assign as the transmission area type for A as the            collection of BS_(i)-lists. Thus, any other P₀ subarea            having the same (or substantially similar) collection of            lists of P₀ area types will be viewed as having            approximately the same radio transmission characteristics.

Note that other transmission signal characteristics may be incorporatedinto the transmission area types. For example, thermal noisecharacteristics may be included by providing a third radio coverage area120 partition, P₃, in addition to the partitions of P₁ and P₂ generatedin (23.8.4.1) and (23.8.4.2) respectively. Moreover, the time varyingcharacteristics of (23.8.2) may be incorporated in the transmission areatype frame work by generating multiple versions of the transmission areatypes such that the transmission area type for a given subarea of P₀ maychange depending on the combination of time varying environmentalcharacteristics to be considered in the transmission area types. Forinstance, to account for seasonality, four versions of the partitions P₁and P₂ may be generated, one for each of the seasons, and subsequentlygenerate a (potentially) different partition P₀ for each season.Further, the type and/or characteristics of base station 122 antennasmay also be included in an embodiment of the transmission area type.

Other embodiments of area types are also within the scope of the presentinvention. As mentioned above, each of the first order models 1224 havedefault confidence values associated therewith, and these confidencevalues may be probabilities. More precisely, such probability confidencevalues can be determined as follows. Assume there is a partition of thecoverage area into subareas, each subarea being denoted a “partitionarea.” For each partition area, activate each first order model 1224with historical location data in the Location Signature Data Base 1320(FIG. 6), wherein the historical location data has been obtained fromcorresponding known mobile station locations in the partition area. Foreach first order model, determine a probability of the first order modelgenerating a location hypothesis whose location estimate contains thecorresponding known mobile station location. To accomplish this, assumethe coverage area is partitioned into partition areas A, wherein eachpartition area A is specified as the collection of coverage arealocations such that for each location, the detected wirelesstransmissions between the network base stations and a target mobilestation at the location can be straightforwardly equated with otherlocations of area A. For example, one such partition, P₀, can be definedwherein each partition area A is specified in terms of three sets ofbase station identifiers, namely, (a) the base station identifiers ofthe base stations that can be both detected at each location of A andcan detect a target mobile station at each location, (b) the identifiersfor base stations that can detect a target mobile station at eachlocation of A, but can not be detected by the target mobile station, and(c) the identifiers for base stations that can be detected by a targetmobile station at each location of A, but these base stations can notdetect the target mobile station. That is, two locations, I₁ and I₂, areidentified as being in A if and only if the three sets of (a), (b), and(c) for I₁ are, respectively, identical to the three sets of (a), (b),and (c) for I₂.

Accordingly, assuming the partition P₀ is used, a description can begiven as to how probabilities may be assigned as the confidence valuesof location hypotheses generated by the first order models 1224. Foreach partition area A, a first order model 1224 is supplied withwireless measurements of archived location data in the LocationSignature Data Base associated with corresponding verified mobilestation locations. Thus, a probability can be determined as to howlikely the first order model is to generate a location hypothesis havinga location estimate containing the corresponding verified mobile stationlocation. Accordingly, a table of partition area probabilities can bedetermined for each first order model 1224. Thus, when a locationhypothesis is generated and identified as belonging to one of thepartition areas, the corresponding probability for that partition areamay be assigned as the confidence value for the location hypothesis. Theadvantages to using actual probabilities here is that, as will bediscussed below, the most likelihood estimator 1344 can compute astraightforward probability for each distinct intersection of themultiple location hypotheses generated by the multiple first ordermodels, such that each such probability indicates a likelihood that thetarget mobile station is in the corresponding intersection.

Location Information Data Bases and Data

Location Data Bases Introduction

It is an aspect of the present invention that MS location processingperformed by the location center/gateway 142 should become increasinglybetter at locating a target MS 140 both by (a) building an increasinglymore detailed model of the signal characteristics of locations in theservice area for the present invention, and also (b) by providingcapabilities for the location center processing to adapt toenvironmental changes.

One way these aspects of the present invention are realized is byproviding one or more data base management systems and data bases for

(a) storing and associating wireless MS signal characteristics withknown locations of MSs 140 used in providing the signal characteristics.Such stored associations may not only provide an increasingly bettermodel of the signal characteristics of the geography of the servicearea, but also provide an increasingly better model of more changeablesignal characteristic affecting environmental factors such as weather,seasons, and/or traffic patterns;

(b) adaptively updating the signal characteristic data stored so that itreflects changes in the environment of the service area such as, forexample, a new high rise building or a new highway.

Referring again to FIG. 5 of the collective representation of these databases is the location information data bases 1232. Included among thesedata bases is a data base for providing training and/or calibration datato one or more trainable/calibratable FOMs 1224, as well as an archivaldata base for archiving historical MS location information related tothe performance of the FOMs. These data bases will be discussed asnecessary hereinbelow. However, a further brief introduction to thearchival data base is provided here. Accordingly, the term, “locationsignature data base” is used hereinafter to denote the archival database and/or data base management system depending on the context of thediscussion. The location signature data base (shown in, for example,FIG. 6 and labeled 1320) is a repository for wireless signalcharacteristic data derived from wireless signal communications betweenan MS 140 and one or more base stations 122, wherein the correspondinglocation of the MS 140 is known and also stored in the locationsignature data base 1320. More particularly, the location signature database 1320 associates each such known MS location with the wirelesssignal characteristic data derived from wireless signal communicationsbetween the MS 140 and one or more base stations 122 at this MSlocation. Accordingly, it is an aspect of the present invention toutilize such historical MS signal location data for enhancing thecorrectness and/or confidence of certain location hypotheses as will bedescribed in detail in other sections below.

Data Representations for the Location Signature Data Base

In one embodiment, there are four fundamental entity types (or objectclasses in an object oriented programming paradigm) utilized in thelocation signature data base 1320. Briefly, these data entities aredescribed in the items (24.1) through (24.4) that follow.

(24.1) (verified) location signatures: Each such (verified) locationsignature describes the wireless signal characteristic measurementsbetween a given base station (e.g., BS 122 or LBS 152) and an MS 140 ata (verified or known) location associated with the (verified) locationsignature. That is, a verified location signature corresponds to alocation whose coordinates such as latitude-longitude coordinates areknown, while simply a location signature may have a known or unknownlocation corresponding with it. Note that the term (verified) locationsignature is also denoted by the abbreviation, “(verified) loc sig”hereinbelow;(24.2) (verified) location signature clusters: Each such (verified)location signature cluster includes a collection of (verified) locationsignatures corresponding to all the location signatures between a targetMS 140 at a (possibly verified) presumed substantially stationarylocation and each BS (e.g., 122 or 152) from which the target MS 140 candetect the BS's pilot channel regardless of the classification of the BSin the target MS (i.e., for CDMA, regardless of whether a BS is in theMS's active, candidate or remaining base station sets, as one skilled inthe art will understand). Note that for simplicity here, it is presumedthat each location signature cluster has a single fixed primary basestation to which the target MS 140 synchronizes or obtains its timing;(24.3) “composite location objects (or entities)”: Each such entity is amore general entity than the verified location signature cluster. Anobject of this type is a collection of (verified) location signaturesthat are associated with the same MS 140 at substantially the samelocation at the same time and each such loc sig is associated with adifferent base station. However, there is no requirement that a loc sigfrom each BS 122 for which the MS 140 can detect the BS's pilot channelis included in the “composite location object (or entity)”; and(24.4) MS location estimation data that includes MS location estimatesoutput by one or more MS location estimating first order models 1224,such MS location estimate data is described in detail hereinbelow.

It is important to note that a loc sig is, in one embodiment, aninstance of the data structure containing the signal characteristicmeasurements output by the signal filtering and normalizing subsystemalso denoted as the signal processing subsystem 1220 describing thesignals between: (i) a specific base station 122 (BS) and (ii) a mobilestation 140 (MS), wherein the BS's location is known and the MS'slocation is assumed to be substantially constant (during a 2–5 secondinterval in one embodiment of the present invention), duringcommunication with the MS 140 for obtaining a single instance of loc sigdata, although the MS location may or may not be known. Further, fornotational purposes, the BS 122 and the MS 140 for a loc sig hereinafterwill be denoted the “BS associated with the loc sig”, and the “MSassociated with the loc sig” respectively. Moreover, the location of theMS 140 at the time the loc sig data is obtained will be denoted the“location associated with the loc sig” (this location possibly beingunknown).

Note that additional description of this aspect of the present inventioncan be found in one of the following two copending U.S. patentapplications which are incorporated herein by reference: (a) “LocationOf A Mobile Station” filed Nov. 24, 1999 having application Ser. No.09/194,367 whose inventors are D. J. Dupray and C. L. Karr, and (b) “AWireless Location System For Calibrating Multiple Location Estimators”filed Oct. 21, 1998 having application Ser. No. 09/176,587 whoseinventor is D. J. Dupray, wherein these copending patent applicationsmay have essential material for the present specification. Inparticular, these copending patent applications may have essentialmaterial relating to the location signature data base 1320.

Location Center Architecture

Overview of Location Center/Gateway Functional Components

FIG. 5 presents a high level diagram of an embodiment of the locationcenter/gateway 142 and the location engine 139 in the context of theinfrastructure for the entire location system of the present invention.

It is important to note that the architecture for the locationcenter/gateway 142 and the location engine 139 provided by the presentinvention is designed for extensibility and flexibility so that MS 140location accuracy and reliability may be enhanced as further locationdata become available and as enhanced MS location techniques becomeavailable. In addressing the design goals of extensibility andflexibility, the high level architecture for generating and processingMS location estimates may be considered as divided into the followinghigh level functional groups described hereinbelow.

Low Level Wireless Signal Processing Subsystem for Receiving andConditioning Wireless Signal Measurements

A first functional group of location engine 139 modules is forperforming signal processing and filtering of MS location signal datareceived from a conventional wireless (e.g., CDMA) infrastructure, asdiscussed in the steps (23.1) and (23.2) above. This group is denotedthe signal processing subsystem 1220 herein. One embodiment of such asubsystem is described in the U.S. copending patent application titled,“Wireless Location Using A Plurality of Commercial NetworkInfrastructures,” by F. W. LeBlanc, Dupray and Karr filed Jan. 22, 1999and having U.S. Pat. No. 6,236,365. Note that this copending patentapplication is incorporated herein entirely by reference since it maycontain essential material for the present invention. In particular,regarding the signal processing subsystem 20. Note, however, that thesignal processing subsystem may be unnecessary for the gateway 142unless the gateway supplies wireless location signal data to one or moreFOMs.

Initial Location Estimators: First Order Models

A second functional group of modules at least accessible by the locationengine 139 are the FOM 1224 for generating various target MS 140location initial estimates, as described in step (23.3). A briefdescription of some types of first order models is provided immediatelybelow. Note that FIG. 8 illustrates another, more detail view of anembodiment of the location center/gateway 142 for the present invention.In particular, this figure illustrates some of the FOMs 1224 at leastaccessible (but not necessarily co-located with the other locationcenter/gateway modules shown in this figure), and additionallyillustrates the primary communications with other modules of thegateway. However, it is important to note that the present invention isnot limited to the FOMs 1224 shown and discussed herein. That is, it isa primary aspect of the present invention to easily incorporate FOMsusing other signal processing and/or computational location estimatingtechniques than those presented herein. Further, note that each FOM typemay have a plurality of its MS location estimating models (at least)accessible by the gateway 142.

For example, (as will be described in further detail below), one suchtype of model or FOM 1224 (hereinafter models of this type are referredto as “terrestrial communication station offset (TCSO) models” or“terrestrial communication station offset (TCSO) first order models”, or“terrestrial communication station offset (TCSO) FOMs”) may be based ona range, offset, and/or distance computation such as on a base stationsignal reception angle determination between the target MS 140 from eachof one or more base stations. Basically, such TCSO models 1224 determinea location estimate of the target MS 140 by determining an offset fromeach of one or more base stations 122, possibly in a particulardirection from each (some of) the base stations, so that, e.g., anintersection of each area locus defined by the base station offsets mayprovide an estimate of the location of the target MS. TCSO FOMs 1224 maycompute such offsets based on, e.g.:

-   -   (a) signal timing measurements between the target mobile station        140 and one or more base stations 122; e.g., timing measurements        such as time difference of arrival (TDOA), or time of arrival        (TOA). Note that both forward and reverse signal path timing        measurements may be utilized;    -   (b) signal strength measurements (e.g., relative to power        control settings of the MS 140 and/or one or more BS 122);        and/or    -   (c) signal angle of arrival measurements, or ranges thereof, at        one or more base stations 122 (such angles and/or angular ranges        provided by, e.g., base station antenna sectors having angular        ranges of 120° or 60°, or, so called “SMART antennas” with        variable angular transmission ranges of 2° to 120°).        Accordingly, a terrestrial communication station offset (TCSO)        model may utilize, e.g., triangulation or trilateration to        compute a location hypothesis having either an area location or        a point location for an estimate of the target MS 140.        Additionally, in some embodiments location hypothesis may        include an estimated error.

Another type of FOM 1224 is a statistically based first order model1224, wherein a statistical technique, such as regression techniques(e.g., least squares, partial least squares, principle decomposition),or e.g., Bollenger Bands (e.g., for computing minimum and maximum basestation offsets). In general, models of this type output locationhypotheses determined by performing one or more statistical techniquesor comparisons between the verified location signatures in locationsignature data base 1320, and the wireless signal measurements from atarget MS. Models of this type are also referred to hereinafter as a“stochastic signal (first order) model” or a “stochastic FOM” or a“statistical model.” Of course, statistically based FOMs may be a hybridcombination with another type of FOM such as a TCSO FOM.

Still another type of FOM 1224 is an adaptive learning model, such as anartificial neural net or a genetic algorithm, wherein the FOM may betrained to recognize or associate each of a plurality of locations witha corresponding set of signal characteristics for communications betweenthe target MS 140 (at the location) and the base stations 122. Moreover,typically such a FOM is expected to accurately interpolate/extrapolatetarget MS 140 location estimates from a set of signal characteristicsfrom an unknown target MS 140 location. Models of this type are alsoreferred to hereinafter variously as “artificial neural net models” or“neural net models” or “trainable models” or “learning models.” Notethat a related type of FOM 1224 is based on pattern recognition. TheseFOMs can recognize patterns in the signal characteristics ofcommunications between the target MS 140 (at the location) and the basestations 122 and thereby estimate a location area of the target MS.However, such FOMs may not be trainable.

Yet another type of FOM 1224 can be based on a collection of dispersedlow power, low cost fixed location wireless transceivers (also denoted“location base stations 152” hereinabove) that are provided fordetecting a target MS 140 in areas where, e.g., there is insufficientbase station 122 infrastructure coverage for providing a desired levelof MS 140 location accuracy. For example, it may uneconomical to providehigh traffic wireless voice coverage of a typical wireless base station122 in a nature preserve or at a fair ground that is only populated afew days out of the year. However, if such low cost location basestations 152 can be directed to activate and deactivate via thedirection of a FOM 1224 of the present type, then these location basestations can be used to both location a target MS 140 and also provideindications of where the target MS is not. For example, if there arelocation base stations 152 populating an area where the target MS 140 ispresumed to be, then by activating these location base stations 152,evidence may be obtained as to whether or not the target MS is actuallyin the area; e.g., if the target MS 140 is detected by a location basestation 152, then a corresponding location hypothesis having a locationestimate corresponding to the coverage area of the location base stationmay have a very high confidence value. Alternatively, if the target MS140 is not detected by a location base station 152, then a correspondinglocation hypothesis having a location estimate corresponding to thecoverage area of the location base station may have a very lowconfidence value. Models of this type are referred to hereinafter as“location base station models.”

Yet another type of FOM 1224 can be based on input from a mobile basestation 148, wherein location hypotheses may be generated from target MS140 location data received from the mobile base station 148.

Still other types of FOM 1224 can be based on various techniques forrecognizing wireless signal measurement patterns and associatingparticular patterns with locations in the coverage area 120. Forexample, artificial neural networks or other learning models can used asthe basis for various FOMs.

Note that the FOM types mentioned here as well as other FOM types arediscussed in detail hereinbelow. Moreover, it is important to keep inmind that in one embodiment of the present invention, the substantiallysimultaneous use or activation of a potentially large number of suchfirst order models 1224, may be able to enhance both the reliability oflocation estimates and the accuracy of such estimates. Additionally,note that in some embodiments of the present invention, the first ordermodels 1224 can be activated when appropriate signal measurements areobtained. For example, a TDOA FOM may be activated when only a singlesignal time delay measurement is obtained from some plurality of basestation 122. However, if, for instance, additional time delay values areobtained (and assuming such additional values are necessary), then oneor more wireless signal pattern matching FOM may also be activated inconjunction with the TDOA FOM. Additionally, a FOM using satellitesignals (e.g., GPS) to perform a triangulation may be activated Wheneverappropriate measurements are received regardless of whether additionalFOMs are capable of being substantially simultaneously activated or not.Accordingly, since such satellite signal FOMs are generally moreaccurate, output from such a FOM may dominate any other previous orsimultaneous estimates unless there is evidence to the contrary.

Moreover, the present invention provides a framework for incorporatingMS location estimators to be subsequently provided as new FOMs in astraightforward manner. For example, a FOM 1224 based on wireless signaltime delay measurements from a distributed antenna system for wirelesscommunication may be incorporated into the present invention for therebylocating a target MS 140 in an enclosed area serviced by the distributedantenna system. Accordingly, by using such a distributed antenna FOM,the present invention may determine the floor of a multi-story buildingfrom which a target MS is transmitting. Thus, MSs 140 can be located inthree dimensions using such a distributed antenna FOM. Additionally,FOMs for detecting certain registration changes within, for example, apublic switched telephone network can also be used for locating a targetMS 140. For example, for some MSs 140 there may be an associated ordedicated device for each such MS that allows the MS to function as acordless phone to a line based telephone network when the device detectsthat the MS is within signaling range. In one use of such a device (alsodenoted herein as a “home base station”), the device registers with ahome location register of the public switched telephone network whenthere is a status change such as from not detecting the corresponding MSto detecting the MS, or visa versa, as one skilled in the art willunderstand. Accordingly, by providing a FOM that accesses the MS statusin the home location register, the location engine 139 can determinewhether the MS is within signaling range of the home base station ornot, and generate location hypotheses accordingly. Moreover, other FOMsbased on, for example, chaos theory and/or fractal theory are alsowithin the scope of the present invention.

It is important to note the following aspects of the present inventionrelating to FOMs 1224:

-   (28.1) Each such first order model 1224 may be relatively easily    incorporated into and/or removed from the present invention. For    example, assuming that the signal processing subsystem 1220 provides    uniform input to the FOMs, and there is a uniform FOM output    interface (e.g., API), it is believed that a large majority (if not    substantially all) viable MS location estimation strategies may be    accommodated. Thus, it is straightforward to add or delete such FOMs    1224.-   (28.2) First order models 1224 may be relatively simple and still    provide significant MS 140 locating functionality and    predictability. For example, much of what is believed to be common    or generic MS location processing has been coalesced into, for    example: a location hypothesis evaluation subsystem, denoted the    hypotheses evaluator 1228 and described immediately below. Thus, the    present invention is modular and extensible such that, for example,    (and importantly) different first order models 1224 may be utilized    depending on the signal transmission characteristics of the    geographic region serviced by an embodiment of the present    invention. Thus, a simple configuration of the present invention may    have (or access) a small number of FOMs 1224 for a simple wireless    signal environment (e.g., flat terrain, no urban canyons and low    population density). Alternatively, for complex wireless signal    environments such as in cites like San Francisco, Tokyo or New York,    a large number of FOMs 1224 may be simultaneously utilized for    generating MS location hypotheses.    An Introduction to an Evaluator for Location Hypotheses: Hypothesis    Evaluator

A third functional group of location engine 139 modules evaluateslocation hypotheses output by the first order models 1224 and therebyprovides a “most likely” target MS location estimate. The modules forthis functional group are collectively denoted the hypothesis evaluator1228.

Hypothesis Evaluator

A primary purpose of the hypothesis evaluator 1228 is to mitigateconflicts and ambiguities related to location hypotheses output by thefirst order models 1224 and thereby output a “most likely” estimate ofan MS for which there is a request for it to be located. In providingthis capability, there are various related embodiments of the hypothesisevaluator that are within the scope of the present invention. Since eachlocation hypothesis includes both an MS location area estimate and acorresponding confidence value indicating a perceived confidence orlikelihood of the target MS being within the corresponding location areaestimate, there is a monotonic relationship between MS location areaestimates and confidence values. That is, by increasing an MS locationarea estimate, the corresponding confidence value may also be increased(in an extreme case, the location area estimate could be the entirecoverage area 120 and thus the confidence value may likely correspond tothe highest level of certainty; i.e., +1.0). Accordingly, given a targetMS location area estimate (of a location hypothesis), an adjustment toits accuracy may be performed by adjusting the MS location area estimateand/or the corresponding confidence value. Thus, if the confidence valueis, for example, excessively low then the area estimate may be increasedas a technique for increasing the confidence value. Alternatively, ifthe estimated area is excessively large, and there is flexibility in thecorresponding confidence value, then the estimated area may be decreasedand the confidence value also decreased. Thus, if at some point in theprocessing of a location hypothesis, if the location hypothesis isjudged to be more (less) accurate than initially determined, then (i)the confidence value of the location hypothesis may be increased(decreased), and/or (ii) the MS location area estimate can be decreased(increased). Moreover, note that when the confidence values areprobabilities, such adjustments are may require the reactivation of oneor more FOMs 1224 with requests to generate location hypotheses havinglocation estimates of different sizes. Alternatively, adjuster modules1436 and/or 1440 (FIG. 16 discussed hereinbelow) may be invoked forgenerating location hypotheses having area estimates of different sizes.Moreover, the confidence value on such an adjusted location hypothesis(actually a new location hypothesis corresponding to the originallygenerated hypothesis) may also be a probability in that combinations ofFOMs 1224 and adjuster modules 1436 and 1440 can also be calibrated forthereby yielding probabilities as confidence values to the resultinglocation hypotheses.

In a first class of embodiments (typically wherein the confidence valuesare not maintained as probabilities), the hypothesis evaluator 1228evaluates location hypotheses and adjusts or modifies only theirconfidence values for MS location area estimates and subsequently usesthese MS location estimates with the adjusted confidence values fordetermining a “most likely” MS location estimate for outputting.Alternatively, in a second class of embodiments for the hypothesisevaluator 1228 (also typically wherein the confidence values are notmaintained as probabilities), MS location area estimates can be adjustedwhile confidence values remain substantially fixed. However, in onepreferred embodiment of the present embodiment, both location hypothesisarea estimates and confidence values are modified.

The hypothesis evaluator 1228 may perform any or most of the followingtasks depending on the embodiment of the hypothesis evaluator. That is,

-   (30.1) it may enhance the accuracy of an initial location hypothesis    generated by an FOM by using the initial location hypothesis as,    essentially, a query or index into the location signature data base    1320 for obtaining one or more corresponding enhanced location    hypotheses, wherein the enhanced location hypotheses have both an    adjusted target MS location area estimates and an adjusted    confidences based on past performance of the FOM in the location    service surrounding the target MS location estimate of the initial    location hypothesis;    Additionally, for embodiments of the hypothesis evaluator 1228    wherein the confidence values for location hypotheses are not    maintained as probabilities, the following additional tasks (30.2)    through (30.7) may be performed:-   (30.2) the hypothesis evaluator 1228 may utilize environmental    information to improve and reconcile location hypotheses supplied by    the first order models 1224. A basic premise in this context is that    the accuracy of the individual first order models may be affected by    various environmental factors such as, for example, the season of    the year, the time of day, the weather conditions, the presence of    buildings, base station failures, etc.;-   (30.3) the hypothesis evaluator 1228 may determine how well the    associated signal characteristics used for locating a target MS    compare with particular verified loc sigs stored in the location    signature data base 1320 (see the location signature data base    section for further discussion regarding this aspect of the    invention). That is, for a given location hypothesis, verified loc    sigs (which were previously obtained from one or more verified    locations of one or more MS's) are retrieved for an area    corresponding to the location area estimate of the location    hypothesis, and the signal characteristics of these verified loc    sigs are compared with the signal characteristics used to generate    the location hypothesis for determining their similarities and    subsequently an adjustment to the confidence of the location    hypothesis (and/or the size of the location area estimate);-   (30.4) the hypothesis evaluator 1228 may determine if (or how well)    such location hypotheses are consistent with well known physical    constraints such as the laws of physics. For example, if the    difference between a previous (most likely) location estimate of a    target MS and a location estimate by a current location hypothesis    requires the MS to:    -   (a1) move at an unreasonably high rate of speed (e.g., 200 mph),        or    -   (b1) move at an unreasonably high rate of speed for an area        (e.g., 80 mph in a corn patch), or    -   (c1) make unreasonably sharp velocity changes (e.g., from 60 mph        in one direction to 60 mph in the opposite direction in 4 sec),        then the confidence in the current Location Hypothesis is likely        to be reduced.

Alternatively, if for example, the difference between a previouslocation estimate of a target MS and a current location hypothesisindicates that the MS is:

-   -   (a2) moving at an appropriate velocity for the area being        traversed, or    -   (b2) moving along an established path (e.g., a freeway),

then the confidence in the current location hypothesis may be increased.

-   (30.5) the hypothesis evaluator 1228 may determine consistencies and    inconsistencies between location hypotheses obtained from different    first order models. For example, if two such location hypotheses,    for substantially the same timestamp, have estimated location areas    where the target MS is likely to be and these areas substantially    overlap, then the confidence in both such location hypotheses may be    increased. Additionally, note that a velocity of an MS may be    determined (via deltas of successive location hypotheses from one or    more first order models) even when there is low confidence in the    location estimates for the MS, since such deltas may, in some cases,    be more reliable than the actual target MS location estimates;-   (30.6) the hypothesis evaluator 1228 determines new (more accurate)    location hypotheses from other location hypotheses. For example,    this module may generate new hypotheses from currently active ones    by decomposing a location hypothesis having a target MS location    estimate intersecting two radically different wireless signaling    area types. Additionally, this module may generate location    hypotheses indicating areas of poor reception; and-   (30.7) the hypothesis evaluator 1228 determines and outputs a most    likely location hypothesis for a target MS.

Note that additional description of the hypothesis evaluator 1228 can befound in one of the following two copending U.S. patent applicationswhich are incorporated herein by reference: (a) “Location Of A MobileStation” filed Nov. 24, 1999 having application Ser. No. 09/194,367whose inventors are D. J. Dupray and C. L. Karr, and (b) “A WirelessLocation System For Calibrating Multiple Location Estimators” filed Oct.21, 1998 having application Ser. No. 09/176,587 whose inventor is D. J.Dupray, wherein these copending patent applications may have essentialmaterial for the present specification. In particular, these copendingpatent applications may have essential material relating to theirdescriptions of the hypothesis evaluator.

Context Adjuster Introduction.

The context adjuster (alternatively denoted “location adjuster modules)1326 module enhances both the comparability and predictability of thelocation hypotheses output by the first order models 1224. In oneembodiment (typically where confidence values of location hypotheses arenot maintained as probabilities), this module modifies locationhypotheses received from the FOMs 1224 so that the resulting locationhypotheses output by the context adjuster 1326 may be further processeduniformly and substantially without concern as to differences inaccuracy between the first order models from which location hypothesesoriginate. Further, embodiments of the context adjuster may determinethose factors that are perceived to impact the perceived accuracy (e.g.,confidence) of the location hypotheses. For instance, environmentalcharacteristics may be taken into account here, such as time of day,season, month, weather, geographical area categorizations (e.g., denseurban, urban, suburban, rural, mountain, etc.), area subcategorizations(e.g., heavily treed, hilly, high traffic area, etc.).

In FIG. 16, two such adjuster modules are shown, namely, an adjuster forenhancing reliability 1436 and an adjuster for enhancing accuracy 1440.Both of these adjusters perform their location hypothesis adjustments inthe manner described above. The difference between these two adjustermodules 1436 and 1440 is primarily the size of the localized area“nearby” the newly generated location estimate. In particular, since itis believed that the larger (smaller) the localized nearby area is, themore likely (less likely) the corresponding adjusted image is to containthe target mobile station location, the adjuster for enhancingreliability 1436 may determine its localized areas “nearby” a newlygenerated location estimate as, for example, having a 40% largerdiameter (alternatively, area) than the location area estimate generatedby a first order model 1224. Alternatively, the adjuster for enhancingaccuracy 1444 may determine its localized areas “nearby” a newlygenerated location estimate as, for example, having a 30% smallerdiameter (alternatively, area) than the location area estimate generatedby a first order model 1224. Thus, each newly generated locationhypothesis can potentially be used to derive at least two additionaladjusted location hypotheses with some of these adjusted locationhypotheses being more reliable and some being more accurate than thelocation hypotheses generated directly from the first order models 1224.

Note that additional description of context adjuster aspects of thepresent invention can be found in the following two copending U.S.patent applications which are incorporated herein by reference: (a)“Location Of A Mobile Station” filed Nov. 24, 1999 having applicationSer. No. 09/194,367 whose inventors are D. J. Dupray and C. L. Karr, and(b) “A Wireless Location System For Calibrating Multiple LocationEstimators” filed Oct. 21, 1998 having application Ser. No. 09/176,587whose inventor is D. J. Dupray, wherein these copending patentapplications may have essential material for the present specification.In particular, these copending patent applications may have essentialmaterial relating to the context adjuster 1326.

MS Status Repository Introduction

The MS status repository 1338 is a run-time storage manager for storinglocation hypotheses from previous activations of the location engine 139(as well as for storing the output “most likely” target MS locationestimate(s)) so that a target MS 140 may be tracked using target MSlocation hypotheses from previous location engine 139 activations todetermine, for example, a movement of the target MS 140 betweenevaluations of the target MS location.

Location Hypothesis Analyzer Introduction.

The location hypothesis analyzer 1332, may adjust confidence values ofthe location hypotheses, according to:

-   -   (a) heuristics and/or statistical methods related to how well        the signal characteristics for the generated target MS location        hypothesis matches with previously obtained signal        characteristics for verified MS locations.    -   (b) heuristics related to how consistent the location hypothesis        is with physical laws, and/or highly probable reasonableness        conditions relating to the location of the target MS and its        movement characteristics. For example, such heuristics may        utilize knowledge of the geographical terrain in which the MS is        estimated to be, and/or, for instance, the MS velocity,        acceleration or extrapolation of an MS position, velocity, or        acceleration.    -   (c) generation of additional location hypotheses whose MS        locations are consistent with, for example, previous estimated        locations for the target MS.

Note that additional description of this aspect of the present inventioncan be found in one of the following copending U.S. patent applicationwhich is incorporated herein by reference: “Location Of A MobileStation” filed Nov. 24, 1999 having application Ser. No. 09/194,367whose inventors are D. J. Dupray and C. L. Karr.

Most Likelihood Estimator

The most likelihood estimator 1344 is a module for determining a “mostlikely” location estimate for a target MS being located by the locationengine 139. The most likelihood estimator 1344 receives a collection ofactive or relevant location hypotheses from the hypothesis analyzer 1332and uses these location hypotheses to determine one or more most likelyestimates for the target MS 140.

There are various embodiments of the most likelihood estimator 1344 thatmay be utilized with the present invention. One such embodiment will nowbe described. At a high level, an area of interest is first determinedwhich contains the target MS 140 whose location is desired. This can bestraightforwardly determined by identifying the base stations 122 thatcan be detected by the target MS 140 and/or the base stations 140 thatcan detect the target MS. Subsequently, assuming that this area ofinterest has been previously partitioned into “cells” (e.g., smallrectangular areas of, for example, 50 to 200 feet per side) and that theresulting location hypotheses for estimating the location of the targetMS 140 each have a likelihood probability associated therewith, then foreach such location hypothesis, a probability (more generally confidencevalue) is capable of being assigned to each cell intersecting and/orincluded in the associated target MS location estimate. In particular,for each location hypothesis, a portion of the probability value, P, forthe associated location estimate, A, can be assigned to each cell, C,intersecting the estimate. One simple way to perform this is to divide Pby the number of cells C, and increment, for each cell C, acorresponding probability indicative of the target MS 140 being in Cwith the result from the division. One skilled in the art will readilyrecognize numerous other ways of incrementing such cell probabilities,including: providing a Gaussian or other probabilistic distribution ofprobability values according to, e.g., the distance of the cell from thecentroid of the location estimate. Accordingly, assuming all suchprobability increments have been assigned to all such cells C from alllocation hypotheses generated for locating the target MS 140, then thefollowing is one embodiment of a program for determining one or moremost likely locations of the target MS.

Desired_rel ← get the desired reliability for the resulting locationestimate; Max_size ← get the desired maximum extent for the resultinglocation estimate; Binned_cells ← sort the cells of the area of interestby their probabilities into bins where each successive bin includesthose cells whose   confidence values are within a smaller(non-overlapping) range from that of any preceding bin. Further, assume  these are, e.g., 100 bins B₁ wherein B₁ has cells with confidenceswithin the range [0, 0.1], and B₁ has cells with   confidences withinthe range [(i − 1) * 0.01, i * 0.01]. Result ← nil; Curr_rel ← 0; /*current likelihood of target MS 140 being in the area represented by“Result” */ Done ← FALSE; Repeat  Cell_bin ← get first (next) bin ofcells from Binned_cells;  While (there are cells in Cell_bin) do  Curr_cell ← get a next cell from Cell_bin that is closest to thecentroid of “Result”;   Result ← Result + Curr_cell;   /* now determinea new reliability value corresponding to adding “Curr_cell” to the mostlikely location    estimate being built in “Result” */   Curr_rel ←Curr_rel + confidence_of_MS_in(Curr_cell);   If(Curr_rel > Desired_rel)then    Done ← TRUE; Until Done; /* reliability that the target MS is in“Result” is sufficient */ Curr_size ← current maximum geographic extent(i.e., dimension) of the area represented by “Result”; If(Curr_size <=Max_size) then output(Result); Else Determine whether “Result” has oneor more outlying cells that can be replaced by other cells closer to thecentroid of “Result” and  still have a reliability >= “Desired_rel”; If(there are replaceable outlier cells) then   replace then in Resultand output(Result);  Else output(Result);

Note that numerous similar embodiments of the above program maybe used,as one skilled in the art will understand. For instance, instead of“building” Result as provided in the above program, Result can be“whittled” from the area of interest. Accordingly, Result would beinitialized to the entire area of interest, and cells would be selectedfor removal from Result. Additionally, note that the above programdetermines a fast approximation to the optimal most likely areacontaining the target MS 140 having at least a particular desiredconfidence. However, a similar program may be readily provided where amost likely area having less than a desired extent or dimension isoutput; e.g., such a program would could be used to provide an answer tothe question: “What city block is the target MS most likely in?”

Additionally, note that a center of gravity type of computation forobtaining the most likely location estimate of the target MS 140 may beused as described in U.S. Pat. No. 5,293,642 ('642 patent) filed Dec.19, 1990 having an issue data of Mar. 8, 1994 with inventor Lo which isincorporated by reference herein and may contain essential material forthe present invention.

Still referring to the hypothesis evaluator 1228, it is important tonote that not all the above mentioned modules are required in allembodiments of the present invention. In particular, the hypothesisanalyzer 1332 may be unnecessary. Accordingly, in such an embodiment,the enhanced location hypotheses output by the context adjuster 1326 areprovided directly to the most likelihood estimator 1344.

Control and Output Gating Modules

A fourth functional group of location engine 139 modules is the controland output gating modules which includes the location center controlsubsystem 1350, and the output gateway 1356. The location controlsubsystem 1350 provides the highest level of control and monitoring ofthe data processing performed by the location center 142. In particular,this subsystem performs the following functions:

-   -   (a) controls and monitors location estimating processing for        each target MS 140. Note that this includes high level exception        or error handling functions;    -   (b) receives and routes external information as necessary. For        instance, this subsystem may receive (via, e.g., the public        telephone switching network and Internet 468) such environmental        information as increased signal noise in a particular service        area due to increase traffic, a change in weather conditions, a        base station 122 (or other infrastructure provisioning), change        in operation status (e.g., operational to inactive);    -   (c) receives and directs location processing requests from other        location centers 142 (via, e.g., the Internet);    -   (d) performs accounting and billing procedures such as billing        according to MS location accuracy and the frequency with which        an MS is located;    -   (e) interacts with location center operators by, for example,        receiving operator commands and providing output indicative of        processing resources being utilized and malfunctions;    -   (f) provides access to output requirements for various        applications requesting location estimates. For example, an        Internet location request from a trucking company in Los Angeles        to a location center 142 in Denver may only want to know if a        particular truck or driver is within the Denver area.        Alternatively, a local medical rescue unit is likely to request        a precise a location estimate as possible.

Note that in FIG. 6, (a)–(d) above are, at least at a high level,performed by utilizing the operator interface 1374.

Referring now to the output gateway 1356, this module routes target MS140 location estimates to the appropriate location application(s). Forinstance, upon receiving a location estimate from the most likelihoodestimator 1344, the output gateway 1356 may determine that the locationestimate is for an automobile being tracked by the police and thereforemust be provided must be provided according to the particular protocol.

System Tuning and Adaptation: the Adaptation Engine

A fifth functional group of location engine 139 modules provides theability to enhance the MS locating reliability and/or accuracy of thepresent invention by providing it with the capability to adapt toparticular operating configurations, operating conditions and wirelesssignaling environments without performing intensive manual analysis ofthe performance of various embodiments of the location engine 139. Thatis, this functional group automatically enhances the performance of thelocation engine for locating MSs 140 within a particular coverage area120 using at least one wireless network infrastructure therein. Moreprecisely, this functional group allows the present invention to adaptby tuning or optimizing certain system parameters according to locationengine 139 location estimate accuracy and reliability.

There are a number location engine 139 system parameters whose valuesaffect location estimation, and it is an aspect of the present inventionthat the MS location processing performed should become increasinglybetter at locating a target MS 140 not only through building anincreasingly more detailed model of the signal characteristics oflocation in the coverage area 120 such as discussed above regarding thelocation signature data base 1320, but also by providing automatedcapabilities for the location center processing to adapt by adjusting or“tuning” the values of such location center system parameters.

Accordingly, the present invention may include a module, denoted hereinas an “adaptation engine” 1382, that performs an optimization procedureon the location center 142 system parameters either periodically orconcurrently with the operation of the location center in estimating MSlocations. That is, the adaptation engine 1382 directs the modificationsof the system parameters so that the location engine 139 increases inoverall accuracy in locating target MSs 140. In one embodiment, theadaptation engine 1382 includes an embodiment of a genetic algorithm asthe mechanism for modifying the system parameters. Genetic algorithmsare basically search algorithms based on the mechanics of naturalgenetics.

Note that additional description of this aspect of the present inventioncan be found in one of the following two copending U.S. patentapplications which are incorporated herein by reference: (a) “LocationOf A Mobile Station” filed Nov. 24, 1999 having application Ser. No.09/194,367 whose inventors are D. J. Dupray and C. L. Karr, and (b) “AWireless Location System For Calibrating Multiple Location Estimators”filed Oct. 21, 1998 having application Ser. No. 09/176,587 whoseinventor is D. J. Dupray, wherein these copending patent applicationsmay have essential material for the present specification. Inparticular, these copending patent applications may have essentialmaterial relating to the use of genetic algorithm implementations foradaptively tuning system parameters of a particular embodiment of thepresent invention.

Implementations of First Order Models

Further descriptions of various first order models 1224 are provided inthis section. However, it is important to note that these are merelyrepresentative embodiments of location estimators that are within thescope of the present invention. In particular, two or more of thewireless location technologies described hereinbelow may be combined tocreated additional First Order Models. For example, varioustriangulation techniques between a target MS 140 and the base stationinfrastructure (e.g., time difference of arrival (TDOA) or time ofarrival (TOA)), may be combined with an angle of arrival (AOA)technique. For instance, if a single direct line of sight anglemeasurement and a single direct line of sight distance measurementdetermined by, e.g., TDOA or TOA can effectively location the target MS140. In such cases, the resulting First Order Models may be morecomplex. However, location hypotheses may generated from such modelswhere individually the triangulation techniques and the AOA techniqueswould be unable to generate effective location estimates.

Terrestrial Communication Station Offset (TCSO) First Order Models(e.g., TOA/TDOA/AOA)

As discussed in the Location Center Architecture Overview section hereinabove, TCSO models determine a presumed direction and/or distance (moregenerally, an offset) that a target MS 140 is from one or more basestations 122. In some embodiments of TCSO models, the target MS locationestimate(s) generated are obtained using radio signal analysistechniques that are quite general and therefore are not capable oftaking into account the peculiarities of the topography of a particularradio coverage area. For example, substantially all radio signalanalysis techniques using conventional procedures (or formulas) arebased on “signal characteristic measurements” such as:

(a) signal timing measurements (e.g., TOA and TDOA), and/or

(b) signal strength measurements.

Furthermore, such signal analysis techniques are likely predicated oncertain very general assumptions that can not fully account for signalattenuation and multipath due to a particular radio coverage areatopography.

Taking CDMA or TDMA base station network as an example, each basestation (BS) 122 is required to emit a constant signal-strength pilotchannel pseudo-noise (PN) sequence on the forward link channelidentified uniquely in the network by a pilot sequence offset andfrequency assignment. It is possible to use the pilot channels of theactive, candidate, neighboring and remaining sets, maintained in thetarget MS, for obtaining signal characteristic measurements (e.g., TOAand/or TDOA measurements) between the target MS 140 and the basestations in one or more of these sets.

Based on such signal characteristic measurements and the speed of signalpropagation, signal characteristic ranges or range differences relatedto the location of the target MS 140 can be calculated. Using TOA and/orTDOA ranges as exemplary, these ranges can then be input to either theradius—radius multilateration or the time difference multilaterationalgorithms along with the known positions of the corresponding basestations 122 to thereby obtain one or more location estimates of thetarget MS 140. For example, if there are, four base stations 122 in theactive set, the target MS 140 may cooperate with each of the basestations in this set to provide signal arrival time measurements.Accordingly, each of the resulting four sets of three of these basestations 122 may be used to provide an estimate of the target MS 140 asone skilled in the art will understand. Thus, potentially (assuming themeasurements for each set of three base stations yields a feasiblelocation solution) there are four estimates for the location of thetarget MS 140. Further, since such measurements and BS 122 positions canbe sent either to the network or the target MS 140, location can bedetermined in either entity.

Since many of the signal measurements utilized by embodiments of TCSOmodels are subject to signal attenuation and multipath due to aparticular area topography. Many of the sets of base stations from whichtarget MS location estimates are desired may result in either nolocation estimate, or an inaccurate location estimate.

Accordingly, some embodiments of TCSO FOMs may attempt to mitigate suchambiguity or inaccuracies by, e.g., identifying discrepancies (orconsistencies) between arrival time measurements and other measurements(e.g., signal strength), these discrepancies (or consistencies) may beused to filter out at least those signal measurements and/or generatedlocation estimates that appear less accurate. In particular, suchidentifying and filtering may be performed by, for example, an expertsystem residing in the TCSO FOM.

Another approach for enhancing certain location techniques such as TDOAor angle or arrival (AOA) is that of super resolution as disclosed inU.S. Pat. No. 5,890,068 filed on Oct. 3, 1996 having an issue date ofMar. 30, 1999 with inventors Fattouche et. al. which is incorporated byreference herein and which may contain essential material for thepresent invention. In particular, the following portions of the '068patent are particularly important: the Summary section, the DetailedDescription portion regarding FIGS. 12–17, and the section titled“Description Of The Preferred Embodiments Of The Invention.”

Another approach, regardless of the FOM utilized, for mitigating suchambiguity or conflicting MS location estimates is particularly novel inthat each of the target MS location estimates is used to generate alocation hypothesis regardless of its apparent accuracy. Accordingly,these location hypotheses are input to an embodiment of the contextadjuster 1326. In particular, in one context adjuster 1326 embodimenteach location hypothesis is adjusted according to past performance ofits generating FOM 1224 in an area of the initial location estimate ofthe location hypothesis (the area, e.g., determined as a function ofdistance from this initial location estimate), this alternativeembodiment adjusts each of the location hypotheses generated by a firstorder model according to a past performance of the model as applied tosignal characteristic measurements from the same set of base stations122 as were used in generating the location hypothesis. That is, insteadof only using only an identification of the first order model (i.e., itsFOM_ID) to, for example, retrieve archived location estimates generatedby the model in an area of the location hypothesis' estimate (whendetermining the model's past performance), the retrieval retrieves thearchived location estimates that are, in addition, derived from thesignal characteristics measurement obtained from the same collection ofbase stations 122 as was used in generating the location hypothesis.Thus, the adjustment performed by this embodiment of the contextadjuster 1326 adjusts according to the past performance of the distancemodel and the collection of base stations 122 used.

Note in one embodiment, such adjustments can also be implemented using aprecomputed vector location error gradient field. Thus, each of thelocation error vectors (as determined by past performance for the FOM)of the gradient field has its starting location at a location previouslygenerated by the FOM, and its vector head at a corresponding verifiedlocation where the target MS 140 actually was. Accordingly, for alocation hypothesis of an unknown location, this embodiment determinesor selects the location error vectors having starting locations within asmall area (e.g., possibly of a predetermined size, but alternatively,dependent on the density of the location error vector starting locationsnearby to the location hypothesis) of the location hypothesis.Additionally, the determination or selection may also be based upon asimilarity of signal characteristics also obtained from the target MS140 being located with signal characteristics corresponding to thestarting locations of location error vectors of the gradient field. Forexample, such sign characteristics may be, e.g., time delay/signalstrength multipath characteristics.

Angle of Arrival First Order Model

Various mobile station location estimating models can be based on theangle of arrival (AOA) of wireless signals transmitted from a target MS140 to the base station infrastructure as one skilled in the art willunderstand. Such AOA models (sometimes also referred to as direction ofarrival or DOA models) typically require precise angular measurements ofthe wireless signals, and accordingly utilize specialized antennas atthe base stations 122. The determined signal transmission angles aresubject to multipath aberrations. Therefore, AOA is most effective whenthere is an unimpeded line-of-sight simultaneous transmission betweenthe target MS 140 and at least two base stations 122.

TCSO (Grubeck) FOM with Increased Accuracy Via Multiple MS Transmissions

Another TCSO first order model 1224, denoted the Grubeck model (FOM)herein, is disclosed in U.S. Pat. No. 6,009,334 filed Nov. 26, 1997 andissued Dec. 28, 1999 having Grubeck, Fischer, and Lundqvist asinventors, this patent being fully incorporated herein by reference. TheGrubeck model includes a location estimator for determining moreaccurately the distance between a wireless receiver at (RX), e.g., aCMRS fixed location communication station (such as a BS 122) and atarget MS 140, wherein wireless signals are repeatedly transmitted fromthe target MS 140 and may be subject to multipath. An embodiment of theGrubeck model may be applied to TOA, TDOA, and/or AOA wirelessmeasurements. For the TOA case, the following steps are performed:

-   -   (a) transmitting “M” samples s_(i) 1<=I<=M of the same wireless        signal from, e.g., the target MS 140 to the RX. Preferably M is        on the order of 50 to 100 (e.g., 70) wireless signal bursts,        wherein each such burst contains a portion having an identical        known contents of bits (denoted a training sequence). However,        note that a different embodiment can use (e.g., 70) received        bursts containing different (non-identical) information, but        information still known to the RX;    -   (b) receiving the “M” signal samples s_(i) along with multipath        components and noise at, e.g., RX;    -   (c) for each of the received “M” samples s_(i), determining at        the RX an estimated channel power profile (CPPi). Each CPPi is        determined by first determining, via a processor at the RX, a        combined correlation response (“Channel Impulse Response” or        CIRi) of a small number of the bursts (e.g., 5) by correlating        each burst with its known contents. Accordingly; the squared        absolute value of the CIRi is the “estimated channel power        profile” or CPPi;    -   (d) (randomly) selecting “N” (e.g., 10) out of the “M” received        samples;    -   (e) performing incoherent integration of the CPPi for the “N”        samples selected, which results in an integrated signal, i.e.,        one integrated channel power profile_ICPP(Ni);    -   (f) determining if the signal-to-noise quality of the ICPP(Ni)        is greater than or equal to a predetermined threshold value, and        if not, improving the signal-to-noise quality of ICPP(Ni) as        required, by redoing the incoherent integration with        successively one additional received sample CPPi until the        signal-to-noise quality of the ICPP(Ni) is greater than or equal        to the predetermined threshold value;    -   (g) determining the TOA(i), including the case of determining        TOA(i) from the maximum signal amplitude;    -   (h) entering the determined TOA(i) value into a diagram that        shows a frequency of occurrence as a function of TOA(i);    -   (i) repeating the whole procedure “X” times by selecting a new        combination of “N” out of “M” samples, which results in “X”        additional points in the frequency of occurrence diagram;    -   (j) reading the minimum value TOA(min) as the time value having        “z” of all occurrences with higher TOA(i) values and “1−z” of        all occurrences with lower TOA(i) values, where z>0.7.

As mentioned above, an embodiment of the Grubeck FOM may also beprovides for TDOA and/or AOA wireless location techniques, wherein asimilar incoherent integration may be performed.

Note that a Grubeck FOM may be particularly useful for locating a targetMS 140 in a GSM wireless network.

TCSO (Parl) FOM Using Different Tones and Multiple Antennas at BSs 122

A first order model 1224, denoted the Pad model herein, is substantiallydisclosed in U.S. Pat. No. 5,883,598 (denoted the '598 patent herein)filed Dec. 15, 1995 and issued Mar. 16, 1999 having Parl, Bussgang,Weitzen and Zagami as inventors, this patent being fully incorporatedherein by reference. The Parl FOM includes a system for receivingrepresentative signals (denoted also “locating signal(s)”) from thetarget MS 140 via, e.g., base stations 122, and subsequently combininginformation regarding the amplitude and phase of the MS transmittedsignals received at the base stations to determine the position of thetarget MS 140. In one embodiment, the Parl model uses input from alocating signal having two or more single-frequency tones, as oneskilled in the art will understand. Moreover, at least some of the basestations 122 preferably include at least two antennas spaced from eachother by a distance between a quarter wavelength and several wavelengthsof the wireless locating signals received from the target MS 140.Optionally, another antenna vertically above or below the two or moreantennas also spaced by a distance of between a quarter wavelength andseveral wavelengths can be used where elevation is also being estimated.The base stations 122 sample locating signals from the target MS 140.The locating signals include tones that can be at different frequencies.The tones can also be transmitted at different times, or, in analternative embodiment, they can be transmitted simultaneously. Because,in one embodiment, only single-frequency tones are used as the locatingsignal instead of modulated signals, substantial transmission circuitrymay be eliminated. The Parl FOM extracts information from eachrepresentative signal received from a target MS 144, wherein at leastsome of the extracted information is related to the amplitude and phaseof the received signal.

In one embodiment of a Parl FOM, related to the disclosure in the '598patent, when the locations of the BSs 122 are known, and the directionfrom any two of the BSs 122 to the target MS 140, the MS's location canbe initially (roughly) determined by signal direction findingtechniques. For example, an estimate of the phase difference between thesignals at a pair of antennas at any BS 122 (having two such antennas)can lead to the determination of the angle from the base station to thetarget MS 140, and thus, the determination of the target MS direction.Subsequently, an enhanced location of the target MS 140 is computeddirectly from received target MS signal data using an ambiguity functionA(x,y) described in the '598 patent, wherein for each point at x,y, theambiguity function A(x,y) depends upon the probability that the MS islocated at the geolocation represented by (x,y). Essentially the ParlFOM combines angle of arrival related data and TDOA related data forobtaining an optimized estimate of the target MS 140. However, itappears that independent AOA and TDOA MS locations are not used indetermining a resulting target MS location (e.g., without the need forprojecting lines at angles of arrival or computing the intersection ofhyperbolas defined by pairs of base stations). Instead, the Parl FOMestimates the target MS's location by minimizes minimizing a jointprobability of location related errors. In particular, such minimizationmay use the mean square error, and the location (x, y) at whichminimization occurs is taken as the estimate of the target MS 140. Inparticular, the ambiguity function A(x,y) defines the error involved ina position determination for each point in a geolocation Cartesiancoordinate system. The Parl model optimizes the ambiguity function toselect a point x,y at which the associated error is minimized. Theresulting location for (x, y) is taken as the estimate of the locationof the target MS 140. Any of several different optimization procedurescan be used to optimize the ambiguity function A(x,y). E.g., a firstrough estimate of the target MS's location may be obtained by directionfinding (as discussed above). Next, six points x,y may be selected thatare in close proximity to the estimated point. The ambiguity functionA(x,y) is solved for each of the x,y points to obtain six values. Thesix computed values are then used to define a parabolic surface. Thepoint x,y at which the maximum value of the parabolic surface occurs isthen taken as the estimate of the target MS 140. However, otheroptimization techniques may also be used. For example, a standardtechnique such as an iterative progression through trial and error toconverge to the maximum can be used. Also, gradient search can be usedto optimize the ambiguity function. In the case of three-dimensionallocation, the two-dimensional ambiguity function A(x,y) is extended to athree-dimensional function A(x,y,z). As in the two-dimensional case, theambiguity function may be optimized to select a point x,y,z as the bestestimate of the target MS's location in three dimensions. Again, any ofseveral known optimization procedures, such as iterative progressionthrough trial and error, gradient search, etc., can be used to optimizethe ambiguity function.

TCSO FOM Using TDOA/AOA Measurements from an MBS 148 and/or an LBS 152

It is believed clear from the location center/gateway 142 architectureand from the architecture of the mobile station location subsystem(described in a separate section hereinbelow) that target MS 140location related information can be obtained from an MBS 148 and/or oneor more LBSs 152. Moreover, such location related information can besupplied to any FOM 1224 that is able to accept such information asinput. Thus, pattern recognition and adaptive FOMs may accept suchinformation. However, to provide an alternative description of how MSlocation related information from an MBS and/or LBS may be used,reference is made to U.S. Pat. No. 6,031,490 (denoted the '490 patentherein) filed Dec. 23, 1997 and issued Feb. 29, 2000 having Forssen,Berg and Ghisler as inventors, this patent being fully incorporatedherein by reference. A TCSO FOM (denoted the FORSSEN FOM herein) usingTDOA/AOA is disclosed in the '490 patent.

The FORSSEN FOM includes a location estimator for determining the TimeDifference of Arrival (TDOA) of the position of a target MS 140, whichis based on Time of Arrival (TOA) and/or AOA measurements. This FOM usesdata received from “measuring devices” provided within a wirelesstelecommunications network. The measuring devices measure TOA on demandand (optionally) Direction of Arrival (DOA), on a digital uplink timeslot or on digital information on an analog uplink traffic channel inone or more radio base stations. The TOA and DOA information and thetraffic channel number are reported to a Mobile Services SwitchingCenter (MSC), which obtains the identity of the target MS 140 from thetraffic channel number and sends the target MS 140 identity and TOA andDOA measurement information to a Service Node (e.g., location center142) of the network. The Service Node calculates the position of thetarget MS 140 using the TOA information (supplemented by the DOAinformation when available). Note, that the TCSO model may utilize datafrom a second mobile radio terminal that is colocated on a mobileplatform (auto, emergency vehicle, etc.) with one of the radio basestations (e.g., MBS 148), which can be moved into relatively closeproximity with the target MS 140. Consequently, by moving one of theradio base stations (MBSs) close to the region of interest (near thetarget MS 140), the position determination accuracy is significantlyimproved.

Note that the '490 patent also discloses techniques for rising thetarget MS's transmission power for thereby allowing wireless signalsfrom the target MS to be better detected by distant BSs 122.

Coverage Area First Order Model

Radio coverage area of individual base stations 122 may be used togenerate location estimates of the target MS 140. Although a first ordermodel 1224 based on this notion may be less accurate than othertechniques, if a reasonably accurate RF coverage area is known for each(or most) of the base stations 122, then such a FOM (denoted hereinafteras a “coverage area first order model” or simply “coverage area model”)may be very reliable. To determine approximate maximum radio frequency(RF) location coverage areas, with respect to BSs 122, antennas and/orsector coverage areas, for a given class (or classes) of (e.g., CDMA orTDMA) mobile station(s) 140, location coverage should be based on anMS's ability to adequately detect the pilot channel, as opposed toadequate signal quality for purposes of carrying user-acceptable trafficin the voice channel. Note that more energy is necessary for trafficchannel activity (typically on the order of at least −94 to −104 dBmreceived signal strength) to support voice, than energy needed to simplydetect a pilot channel's presence for location purposes (typically amaximum weakest signal strength range of between −104 to −110 dBm), thusthe “Location Coverage Area” will generally be a larger area than thatof a typical “Voice Coverage Area”, although industry studies have foundsome occurrences of “no-coverage” areas within a larger covered area

The approximate maximum RF coverage area for a given sector of (moregenerally angular range about) a base station 122 may be represented asa set of points representing a polygonal area (potentially with, e.g.,holes therein to account for dead zones and/or notches). Note that ifsuch polygonal RF coverage area representations can be reliablydetermined and maintained over time (for one or more BS signal powerlevel settings), then such representations can be used in providing aset theoretic or Venn diagram approach to estimating the location of atarget MS 140. Coverage area first order models utilize such anapproach.

One embodiment, a coverage area model utilizes both the detection andnon-detection of base stations 122 by the target MS 140 (conversely, ofthe MS by one or more base stations 122) to define an area where thetarget MS 140 may likely be. A relatively straightforward application ofthis technique is to:

-   -   (a) find all areas of intersection for base station RF coverage        area representations, wherein: (i) the corresponding base        stations are on-line for communicating with MSs 140; (ii) the RF        coverage area representations are deemed reliable for the power        levels of the on-line base stations; (iii) the on-line base        stations having reliable coverage area representations can be        detected by the target MS; and

(iv) each intersection must include a predetermined number of thereliable RF coverage area representations (e.g., 2 or 3); and

-   -   (b) obtain new location estimates by subtracting from each of        the areas of intersection any of the reliable RF coverage area        representations for base stations 122 that can not be detected        by the target MS.

Accordingly, the new areas may be used to generate location hypotheses.

Satellite Signal Triangulation First Order Models

As mentioned hereinabove, there are various satellite systems that maybe used to provide location estimates of a target MS 140 (e.g., GPS,GLONASS, LEOs, and MEOs). In many cases, such location estimates can bevery accurate, and accordingly such accuracy would be reflected in thepresent invention by relatively high confidence values for the locationhypotheses generated from such models in comparison to other FOMs.However, it may be difficult for the target MS 140 to detect and/or lockonto such satellite signals sufficiently well to provide a locationestimate. For example, it may be very unlikely that such satellitesignals can be detected by the MS 140 in the middle of high riseconcrete buildings or parking structures having very reduced exposure tothe sky.

Hybrid Satellite and TCSO FOMs

A first order model 1224, denoted the WATTERS FOM herein, is disclosedin U.S. Pat. No. 5,982,324 filed May 14, 1998 and issued Nov. 9, 1999having Watters, Strawczynski, and Steer as inventors, this patent beingfully incorporated herein by reference. The WATTERS FOM includes alocation estimator for determining the location of a target MS 140 usingsatellite signals to the target MS 140 as well as delay in wirelesssignals communicated between the target MS and base stations 122. Forexample, aspects of global positioning system (GPS) technology andcellular technology are combined in order to locate a target MS 140. TheWATTERS FOM may be used to determine target MS location in a wirelessnetwork, wherein the network is utilized to collect differential GPSerror correction data, which is forwarded to the target MS 140 via thewireless network. The target MS 140 (which includes a receiver R forreceiving non-terrestrial wireless signals from, e.g., GPS, or othersatellites, or even airborne craft) receives this data, along with GPSpseudoranges using its receiver R, and calculates its position usingthis information. However, when the requisite number of satellites arenot in view of the MS 140, then a pseudosatellite signal, broadcast froma BS 122 of the wireless network, is received by the target MS 140 andprocessed as a substitute for the missing satellite signal.Additionally, in at least some circumstances, when the requisite numberof satellites (more generally, non-terrestrial wireless transmitters)are not detected by the receiver R, then the target MS's location iscalculated using the wireless network infrastructure via TDOA/TOA withthe BSs 122 of the network. When the requisite number of satellites(more generally, non-terrestrial wireless transmitters) are againdetected by the receiver R, then the target MS is again calculated usingwireless signals from the non-terrestrial wireless transmitters.Additionally, the WATTERS FOM may use wireless signals already beingtransmitted from base stations 122 to the target MS 140 in a wirelessnetwork to calculate a round trip time delay, from which a distancecalculation between the base station and the target MS can be made. Thisdistance calculation substitutes for a missing non-terrestrialtransmission signal.

Location Base Station First Order Model

In the location base station (LBS) model (FOM 1224), a database isaccessed which contains electrical, radio propagation and coverage areacharacteristics of each of the location base stations in the radiocoverage area. The LBS model is an active model, in that it can probe orexcite one or more particular LBSs 152 in an area for which the targetMS 140 to be located is suspected to be placed. Accordingly, the LBSmodel may receive as input a most likely target MS 140 location estimatepreviously output by the location engine 139 of the present invention,and use this location estimate to determine which (if any) LBSs 152 toactivate and/or deactivate for enhancing a subsequent location estimateof the target MS. Moreover, the feedback from the activated LBSs 152 maybe provided to other FOMs 1224, as appropriate, as well as to the LBSmodel. However, it is an important aspect of the LBS model that when itreceives such feedback, it may output location hypotheses havingrelatively small target MS 140 location area estimates about the activeLBSs 152 and each such location hypothesis also has a high confidencevalue indicative of the target MS 140 positively being in thecorresponding location area estimate (e.g., a confidence value of 0.9 to+1), or having a high confidence value indicative of the target MS 140not being in the corresponding location area estimate (i.e., aconfidence value of −0.9 to −1). Note that in some embodiments of theLBS model, these embodiments may have functionality similar to that ofthe coverage area first order model described above. Further note thatfor LBSs within a neighborhood of the target MS wherein there is areasonable chance that with movement of the target MS may be detected bythese LBSs, such LBSs may be requested to periodically activate. (Note,that it is not assumed that such LBSs have an on-line external powersource; e.g., some may be solar powered). Moreover, in the case where anLBS 152 includes sufficient electronics to carry voice communicationwith the target MS 140 and is the primary BS for the target MS (oralternatively, in the active or candidate set), then the LBS model willnot deactivate this particular LBS during its procedure of activatingand deactivating various LBSs 152.

Stochastic First Order Model

The stochastic first order models may use statistical predictiontechniques such as principle decomposition, partial least squares,partial least squares, or other regression techniques for predicting,for example, expected minimum and maximum distances of the target MSfrom one or more base stations 122, e.g., Bollenger Bands. Additionally,some embodiments may use Markov processes and Random Walks (predictedincremental MS movement) for determining an expected area within whichthe target MS 140 is likely to be. That is, such a process measures theincremental time differences of each pilot as the MS moves forpredicting a size of a location area estimate using past MS estimatessuch as the verified location signatures in the location signature database 1320.

Pattern Recognition and Adaptive First Order Models

It is a particularly important aspect of the present invention toprovide:

-   -   (a) one or more FOMs 1224 that generate target MS 140 location        estimates by using pattern recognition or associativity        techniques, and/or    -   (b) one or more FOMs 1224 that are adaptive or trainable so that        such FOMs may generate increasingly more accurate target MS        location estimates from additional training.        Statistically Based Pattern Recognition First Order Models

Regarding FOMs 1224 using pattern recognition or associativitytechniques, there are many such techniques available. For example, thereare statistically based systems such as “CART”(acronym forClassification and Regression Trees) by ANGOSS Software InternationalLimited of Toronto, Canada that may be used for automatically fordetecting or recognizing patterns in data that were not provided (andlikely previously unknown). Accordingly, by imposing a relatively finemesh or grid of cells of the radio coverage area, wherein each cell isentirely within a particular area type categorization, such as thetransmission area types (discussed in the section, “Coverage Area: AreaTypes And Their Determination” above), the verified location signatureclusters within the cells of each area type may be analyzed for signalcharacteristic patterns. Accordingly, if such a characteristic patternis found, then it can be used to identify one or more of the cells inwhich a target MS is likely to be located. That is, one or more locationhypotheses may be generated having target MS 140 location estimates thatcover an area having the identified cells wherein the target MS 140 islikely to be located. Further note that such statistically based patternrecognition systems as “CART” include software code generators forgenerating expert system software embodiments for recognizing thepatterns detected within a training set (e.g., the verified locationsignature clusters).

A related statistical pattern recognition FOM 1224 is also disclosed inU.S. Pat. No. 6,026,304, filed Jan. 8, 1997 and issued Feb. 15, 2000,having Hilsenrath and Wax as inventors, this patent (denoted theHilsenrath patent herein) being incorporated herein fully by reference.An embodiment of a FOM 1224 based on the disclosure of the Hilsenrathpatent is referred to herein as the Hilsenrath FOM. The Hilsenrath FOMincludes a wireless location estimator that locates a target MS 140using measurements of multipath signals in order to accurately determinethe location of the target MS 140. More particularly, to locate thetarget MS 140, the Hilsenrath FOM uses wireless measurements of both adirect signal transmission path and multipath transmission signals fromthe MS 140 to a base station 122 receiver. The wireless signals from thetarget MS 140 arrive at and are detected by an antenna array of thereceiver at the BS 122, wherein the antenna array includes a pluralityof antennas. A signal signature (e.g., an embodiment of a locationsignature herein) for this FOM may be derived from any combination ofamplitude, phase, delay, direction, and polarization information of thewireless signals transmitted from the target MS 140 to the base station122 receiver. The Hilsenrath FOM 1224 determines a signal signature froma signal subspace of a covariance matrix. In particular, for p antennasincluded in the base station receiver, these antennas are used toreceive complex signal envelopes x.₁(t), x.₂(t), . . . , x._(p)(t),respectively, which are conventionally grouped together to form ap-dimensional array vector x(t)=[x₁(t), x₂(t), . . . , x._(p)(t)]^(T).The signal subspace may be determined from a collection of M such arrayvectors x(t) by several techniques. In one such technique, the outerproducts of the M vectors are added together to form a pxp signalcovariance matrix, R=1/M [x(t₁)x(t₁)^(H)+ . . . +x (t_(M))x(t_(M))^(H)].The eigenvalues of R whose magnitudes exceed a predetermined thresholddetermine a set of dominant eigenvectors. The signal subspace is thespace spanned by these dominant eigenvectors. The signal signature iscompared to a database of calibrated signal signatures and correspondinglocations (e.g., an embodiment of the location signature data base1320), wherein the signal signatures in the database includerepresentations of the signal subspaces (such as the dominanteigenvectors of the covariance matrices. Accordingly, a location whosecalibrated signature best matches the signal signature of the target MS140 is selected as the most likely location of the target MS 140. Notethat the database of calibrated signal signatures and correspondingverified locations is generated by a calibration procedure in which acalibrating MS 140 transmits location data derived from a co-located GPSreceiver to the base stations 122. Thus, for each of a plurality oflocations distributed through a service area, the location hasassociated therewith: the (GPS or verified) location information and thecorresponding signal signature of the calibrating MS 140.

Accordingly, the location of a target MS 140 in the service area may bedetermined as follows. Signals originating from the target MS 140 at anunknown location are received at a base station 122. A signal processor,e.g., at the base station 122, then determines the signal signature asdescribed above. The signal signature is then compared with thecalibrated signal signatures stored in the above described embodiment ofthe location signature database 1320 during the calibration procedure.Using a measure of difference between subspaces (e.g., an angle betweensubspaces), a set of likely locations is selected from this locationsignature database embodiment. These selected likely locations are thoselocations whose associated calibrated signal signatures differ by lessthan a minimum threshold value from the target MS 140 signal signature.The difference measure is further used to provide a correspondingmeasure of the probability that each of the selected likely locations isthe actual target MS location. Moreover, for one or more of the selectedlikely location, the corresponding measure may be output as theconfidence value for a corresponding location hypothesis output by aHilsenrath FOM 1224.

Thus, an embodiment of the present invention using such a Hilsenrath FOM1224 performs the following steps (a)–(d):

-   -   (a) receiving at an antenna array provided at one of the base        stations 122, signals originating from the target MS 140,        wherein the signals comprise p-dimensional array vectors sampled        from p antennas of the array;    -   (b) determining from the received signals, a signal signature,        wherein the signal signature comprises a measured subspace,        wherein the array vectors x(t) are approximately confined to the        measured subspace;    -   (c) comparing the signal signature to previously obtained (and        similarly computed) signal signatures, wherein each of the        previously obtained signal signatures, SS, has associated        therewith corresponding location data verifying the location        where SS was obtained, wherein this step of comparing comprises        substep of calculating differences between: (i) the measured        subspace, and (ii) a similarly determined subspace for each of a        plurality of the previously obtained signal signatures; and    -   (d) selecting from the previously obtained signal signatures a        most likely signal signature and a corresponding most likely        location of the target MS 140 by using the calculated        differences;

Note that regardless of the reliability some FOMs as described here maynot be exceedingly accurate, but may be very reliable. Thus, since anaspect of at least some embodiments of the present invention is to use aplurality of MS location techniques (FOMs) for generating locationestimates and to analyze the generated estimates (likely after beingadjusted) to detect patterns of convergence or clustering among theestimates, even large MS location area estimates may be useful. Forexample, it can be the case that four different and relatively large MSlocation estimates, each having very high reliability, have an area ofintersection that is acceptably precise and inherits the very highreliability from each of the large MS location estimates from which theintersection area was derived.

Note, that another statistically based FOM 1224 may be provided whereinthe radio coverage area is decomposed substantially as above, but inaddition to using the signal characteristics for detecting useful signalpatterns, the specific identifications of the base station 122 providingthe signal characteristics may also be used. Thus, assuming there is asufficient density of verified location signature clusters in some ofthe mesh cells so that the statistical pattern recognizer can detectpatterns in the signal characteristic measurements, an expert system maybe generated that outputs a target MS 140 location estimate that mayprovide both a reliable and accurate location estimate of a target MS140.

Adaptive/Trainable First Order Models

The term adaptive is used to describe a data processing component thatcan modify its data processing behavior in response to certain inputsthat are used to change how subsequent inputs are processed by thecomponent. Accordingly, a data processing component may be “explicitlyadaptive” by modifying its behavior according to the input of explicitinstructions or control data that is input for changing the component'ssubsequent behavior in ways that are predictable and expected. That is,the input encodes explicit instructions that are known by a user of thecomponent. Alternatively, a data processing component may be “implicitlyadaptive” in that its behavior is modified by other than instructions orcontrol data whose meaning is known by a user of the component. Forexample, such implicitly adaptive data processors may learn by trainingon examples, by substantially unguided exploration of a solution space,or other data driven adaptive strategies such as statistically generateddecision trees. Accordingly, it is an aspect of the present invention toutilize not only explicitly adaptive MS location estimators within FOMs1224, but also implicitly adaptive MS location estimators. Inparticular, artificial neural networks (also denoted neural nets andANNs herein) are used in some embodiments as implicitly adaptive MSlocation estimators within FOMs. Thus, in the sections below, neural netarchitectures and their application to locating an MS is described.

Artificial Neural Networks for MS Location

Artificial neural networks may be particularly useful in developing oneor more first order models 1224 for locating an MS 140, since, forexample, ANNs can be trained for classifying and/or associativelypattern matching of various RF signal measurements such as the locationsignatures. That is, by training one or more artificial neural netsusing RF signal measurements from verified locations so that RF signaltransmissions characteristics indicative of particular locations areassociated with their corresponding locations, such trained artificialneural nets can be used to provide additional target MS 140 locationhypotheses. Moreover, it is an aspect of the present invention that thetraining of such artificial neural net based FOMs (ANN FOMs) is providedwithout manual intervention as will be discussed hereinbelow. Additionaldescription of this aspect of the present invention can be found in thecopending U.S. patent application titled “Location Of A Mobile Station”filed Nov. 24, 1999 having application Ser. No. 09/194,367 whoseinventors are D. J. Dupray and C. L. Karr, which is incorporated hereinby reference and wherein this copending patent application may haveessential material for the present invention. In particular, thiscopending patent application may have essential material relating to theuse of ANNs as mobile station location estimators 1224.

Other First Order Models

U.S. Pat. No. 5,390,339 ('339 patent) filed Oct. 23, 1991 having anissue date of Feb. 14, 1995 with inventor being Bruckert et. al.provides number of embodiments of wireless location estimators forestimating the location of a “remote unit.” In particular, variouslocation estimator embodiments are described in relation to FIGS. 1B and2B therein. The location estimators in the '339 patent are, in general,directed to determining weighted or adjusted distances of the “remoteunit” (e.g., MS 140) from one or more “transceivers” (e.g., basestations 122). The distances are determined using signal strengthmeasurements of wireless signals transmitted between the “remote unit”and the “transceivers.” However, adjustments are in the signal strengthsaccording to various signal transmission anomalies (e.g., co-channelinterference), impairments and/or errors. Additionally, a signal RFpropagation model may be utilized, and a likelihood of the “remote unit”being in the designated coverage areas (cells) of particulartransceivers (e.g., base stations 122) is determined using probabilistictechniques such as posteriori probabilities. Accordingly, the Bruckert'339 patent is fully incorporated by reference herein and may containessential material for the present invention.

U.S. Pat. No. 5,570,412 ('412 patent) filed Sep. 28, 1994 having anissue date of Oct. 29, 1996 with inventors LeBlanc et. al. providefurther embodiments of wireless location estimators that may be used asFirst Order Models 1224. The location estimating techniques of theLeBlanc '412 patent are described with reference to FIG. 8 andsucceeding figures therein. At a high level, wireless locationtechniques of the '412 patent can be characterized by the followingquote therefrom:

-   -   “The location processing of the present invention focuses on the        ability to predict and model RF contours using actual RF        measurements, then performing data reduction techniques such as        curve fitting technique, Bollinger Bands, and Genetic        Algorithms, in order to locate a mobile unit and disseminate its        location.”        Accordingly, the LeBlanc '412 patent is fully incorporated by        reference herein and may contain essential material for the        present invention.

U.S. Pat. No. 5,293,645 ('645 patent) filed Oct. 4, 1991 having an issuedate of Mar. 8, 1994 with inventor Sood. provide further embodiments ofwireless location estimators that may be used as First Order Models1224. In particular, the '645 patent describes wireless locationestimating techniques using triangulations or other geographicalintersection techniques. Further, one such technique is described incolumn 6, line 42 through column 7, line 7. Accordingly, the Sood '645patent is fully incorporated by reference herein and may containessential material for the present invention.

U.S. Pat. No. 5,293,642 ('642 patent) filed Dec. 19, 1990 having anissue data of Mar. 8, 1994 with inventor Lo provide further embodimentsof wireless location estimators that may be used as First Order Models1224. In particular, the '642 patent determines a correspondingprobability density function (pdf) about each of a plurality of basestations in communication with the target MS 140. That is, uponreceiving wireless signal measurements from the transmissions betweenthe target MS 140 and base stations 122, for each BS 122, acorresponding pdf is obtained from prior measurements of a particularwireless signal characteristic at locations around the base station.Subsequently, a most likely location estimation is determined from ajoint probability density function of the individual base stationprobability density functions. Further description can be found in theDescription Of The Preferred Embodiment section of the '642 patent.Accordingly, the Lo '642 patent is incorporated by reference herein andmay contain essential material for the present invention.

Hybrid First Order Models

Time Difference of Arrival and Timing Advance FOM

A first order model 1224 denoted the Yost model herein. The Yost modelincludes a location estimator that uses a combination of Time Differenceof Arrival (TDOA) and Timing Advance (TA) location determiningtechniques for determining the location of a target MS 140, whereinthere are minor modifications to a telecommunication network such as aCMRS. The hybrid wireless location technique utilized by this locationestimator uses TDOA measurements and TA measurements to obtainsubstantially independent location estimates of the target MS 140,wherein the TDOA measurements determine hyperbolae MS loci, about basestations 122 communicating (uni or bi-directionally) with the target MS,and the TA measurements determine circles about the base stations 122.Accordingly, an enhanced location estimate of the MS 140 can be obtainedby using a least squares (or other statistical technique), wherein theleast-squares technique determines a location for the MS between thevarious curves (hyperbolae and circles) that best approximates a pointof intersection. Note that TA is used in all Time Division MultipleAccess (TDMA) systems as one skilled in the art will understand, andmeasurements of TA can provide a measurement of the distance of the MSfrom a TDMA communication station in communication with the target MS140. The Yost model is disclosed in U.S. Pat. No. 5,987,329 ('329patent) filed Jul. 30, 1997 and issued Nov. 16, 1999 having Yost andPanchapakesan as inventors, this patent being fully incorporated hereinfully by reference to thereby further describe the Yost model. Thefollowing quote from the '329 patent describes an important aspect ofthe Yost model:

-   -   “Furthermore, the combination of TA and TDOA allows resolution        of common ambiguities suffered by either technique separately.        For example, in FIG. 5 a situation involving three base stations        24 (A, B and C as described, the latter being visible in the        figure) is represented along with the resultant two hyperbolas        AB and AC (and redundant hyperbola BC) for a TDOA position        determination of the mobile M. FIG. 5 is a magnified view of the        mobile terminal M location showing the nearby base stations and        the nearby portions at the curves. It should be understood that,        in this case, using TDOA alone, there are two possible        solutions, where the hyperbolae cross. The addition of the TA        circles (dashed curves) will allow the ambiguous solutions,        which lie at different TA from all three base stations, to be        clearly resolved without the need for additional base station 24        measurements.”

As an aside note that a timing advance (TA) first order model may beprovided as a separate FOM independent from the TDOA portion of the Yostmodel. Thus, if an embodiment of the present invention includes both aTA FOM and a TDOA FOM, then the multiple location estimator architectureof the present invention may substantially include the Yost modelwhenever the TA FOM and TDOA FOM are both activated for a same locationinstance of a target MS 140. However, it is an aspect of the presentinvention to also activate such a TA FOM and a TDOA FOM asynchronouslyfrom one another.

Satellite and Terrestrial Base Station Hybrid FOM

A first order model 1224, denoted the Sheynblat model (FOM) herein, isdisclosed in U.S. Pat. No. 5,999,124 (denoted the '124 patent herein)filed Apr. 22, 1998 and issued Dec. 7, 1999 having Sheynblat as theinventor, this patent being fully incorporated herein by reference TheSheynblat FOM provides a location estimator for processing target MS 140location related information obtained from: (a) satellite signals of asatellite positioning system (denoted SPS in the '124 patent) (e.g., GPSor GLONASS, LEO positioning satellites, and/or MEO positioningsatellites), and (b) communication signals transmitted in theterrestrial wireless cellular network of BSs 122 for a radio coveragearea, e.g., coverage area 120 (FIG. 4), wherein there is two-waywireless communication between the target MS 140 and the BSs. In oneembodiment of the Sheynblat FOM, the location related informationobtained from the satellite signals includes a representation of a timeof travel of SPS satellite signals from a SPS satellite to acorresponding SPS receiver operatively coupled to (and co-located with)the target MS 140 (such “time of travel” is referred to as a pseudorangeto the SPS satellite), Additionally for this embodiment, the locationrelated information obtained from the communication signals in thewireless cellular network includes time of travel related informationfor a message in the communication signals between a BS 122 transceiverand the target MS 140 (this second “time of travel” related informationis referred to as a cellular pseudorange). Accordingly, variouscombinations of pseudoranges to SPS satellites, and cellularpseudoranges can be used to determine a likely location of the target MS140. As an example, if the target MS 140 (enhanced with a SPS receiver)can receive SPS satellite signals from one satellite, and additionally,the target MS is also in wireless communication (or can be in wirelesscommunication) with two BSs 122, then three pseudoranges may be obtainedand used to determine the position of the target MS by, e.g.,triangulation. Of course, other combinations are possible fordetermining a location of the target MS 140, e.g., pseudoranges to twoSPS satellites and one cellular pseudorange. Additionally, varioustechniques may be used to mitigate the effects of multipath on thesepseudoranges. For example, since it is typical for the target MS 140 todetect (or be detected by) a plurality of BSs 122, a correspondingplurality of cellular pseudoranges may be obtained, wherein suchcellular pseudoranges may be used in a cluster analysis technique todisambiguate MS locations identified by the satellite pseudoranges.Moreover, the determination of a location hypothesis is performed, in atleast one embodiment, at a site remote from the target MS 140, such asthe location center/gateway 142, or another site that communicates withthe location center/gateway for supplying a resulting MS location to thegateway. Alternatively, the target MS 140 may perform the calculationsto determine its own location. Note that this alternative technique maybe particularly useful when the target MS 140 is a mobile base station148.

MS Status Repository Embodiment

The MS status repository 1338 is a run-time storage manager for storinglocation hypotheses from previous activations of the location engine 139(as well as the output target MS location estimate(s)) so that a targetMS may be tracked using target MS location hypotheses from previouslocation engine 139 activations to determine, for example, a movement ofthe target MS between evaluations of the target MS location. Thus, byretaining a moving window of previous location hypotheses used inevaluating positions of a target MS, measurements of the target MS'svelocity, acceleration, and likely next position may be determined bythe location hypothesis analyzer 1332. Further, by providingaccessibility to recent MS location hypotheses, these hypotheses may beused to resolve conflicts between hypotheses in a current activation forlocating the target MS; e.g., MS paths may be stored here for use inextrapolating a new location

Mobile Base Station Location Subsystem Description

Mobile Base Station Subsystem Introduction

Any collection of mobile electronics (denoted mobile location unit) thatis able to both estimate a location of a target MS 140 and communicatewith the base station network may be utilized by the present inventionto more accurately locate the target MS. Such mobile location units mayprovide greater target MS location accuracy by, for example, homing inon the target MS and by transmitting additional MS location informationto the location center 142. There are a number of embodiments for such amobile location unit contemplated by the present invention. For example,in a minimal version, such the electronics of the mobile location unitmay be little more than an onboard MS 140, a sectored/directionalantenna and a controller for communicating between them. Thus, theonboard MS is used to communicate with the location center 142 andpossibly the target MS 140, while the antenna monitors signals forhoming in on the target MS 140. In an enhanced version of the mobilelocation unit, a GPS receiver may also be incorporated so that thelocation of the mobile location unit may be determined and consequentlyan estimate of the location of the target MS may also be determined.However, such a mobile location unit is unlikely to be able to determinesubstantially more than a direction of the target MS 140 via thesectored/directional antenna without further base station infrastructurecooperation in, for example, determining the transmission power level ofthe target MS or varying this power level. Thus, if the target MS or themobile location unit leaves the coverage area 120 or resides in a poorcommunication area, it may be difficult to accurately determine wherethe target MS is located. None-the-less, such mobile location units maybe sufficient for many situations, and in fact the present inventioncontemplates their use. However, in cases where direct communicationwith the target MS is desired without constant contact with the basestation infrastructure, the present invention includes a mobile locationunit that is also a scaled down version of a base station 122. Thus,given that such a mobile base station or MBS 148 includes at least anonboard MS 140, a sectored/directional antenna, a GPS receiver, a scaleddown base station 122 and sufficient components (including a controller)for integrating the capabilities of these devices, an enhancedautonomous MS mobile location system can be provided that can beeffectively used in, for example, emergency vehicles, air planes andboats. Accordingly, the description that follows below describes anembodiment of an MBS 148 having the above mentioned components andcapabilities for use in a vehicle.

As a consequence of the MBS 148 being mobile, there are fundamentaldifferences in the operation of an MBS in comparison to other types ofBS's 122 (152). In particular, other types of base stations have fixedlocations that are precisely determined and known by the locationcenter, whereas a location of an MBS 148 may be known only approximatelyand thus may require repeated and frequent re-estimating. Secondly,other types of base stations have substantially fixed and stablecommunication with the location center (via possibly other BS's in thecase of LBSs 152) and therefore although these BS's may be more reliablein their in their ability to communicate information related to thelocation of a target MS with the location center, accuracy can beproblematic in poor reception areas. Thus, MBSs may be used in areas(such as wilderness areas) where there may be no other means forreliably and cost effectively locating a target MS 140 (i.e., there maybe insufficient fixed location BS's coverage in an area).

FIG. 11 provides a high level block diagram architecture of oneembodiment of the MBS location subsystem 1508. Accordingly, an MBS mayinclude components for communicating with the fixed location BS networkinfrastructure and the location center 142 via an on-board transceiver1512 that is effectively an MS 140 integrated into the locationsubsystem 1508. Thus, if the MBS 148 travels through an area having poorinfrastructure signal coverage, then the MBS may not be able tocommunicate reliably with the location center 142 (e.g., in rural ormountainous areas having reduced wireless telephony coverage). So it isdesirable that the MBS 148 must be capable of functioning substantiallyautonomously from the location center. In one embodiment, this impliesthat each MBS 148 must be capable of estimating both its own location aswell as the location of a target MS 140.

Additionally, many commercial wireless telephony technologies requireall BS's in a network to be very accurately time synchronized both fortransmitting MS voice communication as well as for other services suchas MS location. Accordingly, the MBS 148 will also require such timesynchronization. However, since an MBS 148 may not be in constantcommunication with the fixed location BS network (and indeed may beoff-line for substantial periods of time), on-board highly accuratetiming device may be necessary. In one embodiment, such a device may bea commercially available ribidium oscillator 1520 as shown in FIG. 11.

Since the MBS 148, includes a scaled down version of a BS 122 (denoted1522 in FIG. 11), it is capable of performing most typical BS 122 tasks,albeit on a reduced scale. In particular, the base station portion ofthe MBS 148 can:

-   -   (a) raise/lower its pilot channel signal strength,    -   (b) be in a state of soft hand-off with an MS 140, and/or    -   (c) be the primary BS 122 for an MS 140, and consequently be in        voice communication with the target MS (via the MBS operator        telephony interface 1524) if the MS supports voice        communication.        Further, the MBS 148 can, if it becomes the primary base station        communicating with the MS 140, request the MS to raise/lower its        power or, more generally, control the communication with the MS        (via the base station components 1522). However, since the MBS        148 will likely have substantially reduced telephony traffic        capacity in comparison to a standard infrastructure base station        122, note that the pilot channel for the MBS is preferably a        nonstandard pilot channel in that it should not be identified as        a conventional telephony traffic bearing BS 122 by MS's seeking        normal telephony communication. Thus, a target MS 140 requesting        to be located may, depending on its capabilities, either        automatically configure itself to scan for certain predetermined        MBS pilot channels, or be instructed via the fixed location base        station network (equivalently BS infrastructure) to scan for a        certain predetermined MBS pilot channel.

Moreover, the MBS 148 has an additional advantage in that it cansubstantially increase the reliability of communication with a target MS140 in comparison to the base station infrastructure by being able tomove toward or track the target MS 140 even if this MS is in (or movesinto) a reduced infrastructure base station network coverage area.Furthermore, an MBS 148 may preferably use a directional or smartantenna 1526 to more accurately locate a direction of signals from atarget MS 140. Thus, the sweeping of such a smart antenna 1526(physically or electronically) provides directional informationregarding signals received from the target MS 140. That is, suchdirectional information is determined by the signal propagation delay ofsignals from the target MS 140 to the angular sectors of one of moredirectional antennas 1526 on-board the MBS 148.

Before proceeding to further details of the MBS location subsystem 1508,an example of the operation of an MBS 148 in the context of respondingto a 911 emergency call is given. In particular, this example describesthe high level computational states through which the MBS 148transitions, these states also being illustrated in the state transitiondiagram of FIG. 12. Note that this figure illustrates the primary statetransitions between these MBS 148 states, wherein the solid statetransitions are indicative of a typical “ideal” progression whenlocating or tracking a target MS 140, and the dashed state transitionsare the primary state reversions due, for example, to difficulties inlocating the target MS 140.

Accordingly, initially the MBS 148 may be in an inactive state 1700,wherein the MBS location subsystem 1508 is effectively available forvoice or data communication with the fixed location base stationnetwork, but the MS 140 locating capabilities of the MBS are not active.From the inactive state 1700 the MBS (e.g., a police or rescue vehicle)may enter an active state 1704 once an MBS operator has logged onto theMBS location subsystem of the MBS, such logging being forauthentication, verification and journaling of MBS 148 events. In theactive state 1704, the MBS may be listed by a 911 emergency centerand/or the location center 142 as eligible for service in responding toa 911 request. From this state, the MBS 148 may transition to a readystate 1708 signifying that the MBS is ready for use in locating and/orintercepting a target MS 140. That is, the MBS 148 may transition to theready state 1708 by performing the following steps:

-   -   (1 a) Synchronizing the timing of the location subsystem 1508        with that of the base station network infrastructure. In one        embodiment, when requesting such time synchronization from the        base station infrastructure, the MBS 148 will be at a        predetermined or well known location so that the MBS time        synchronization may adjust for a known amount of signal        propagation delay in the synchronization signal.    -   (1 b) Establishing the location of the MBS 148. In one        embodiment, this may be accomplished by, for example, an MBS        operator identifying the predetermined or well known location at        which the MBS 148 is located.    -   (1 c) Communicating with, for example, the 911 emergency center        via the fixed location base station infrastructure to identify        the MBS 148 as in the ready state.

Thus, while in the ready state 1708, as the MBS 148 moves, it has itslocation repeatedly (re)-estimated via, for example, GPS signals,location center 142S location estimates from the base stations 122 (and152), and an on-board deadreckoning subsystem 1527 having an MBSlocation estimator according to the programs described hereinbelow.However, note that the accuracy of the base station time synchronization(via the ribidium oscillator 1520) and the accuracy of the MBS 148location may need to both be periodically recalibrated according to (1a) and (1 b) above.

Assuming a 911 signal is transmitted by a target MS 140, this signal istransmitted, via the fixed location base station infrastructure, to the911 emergency center and the location center 142, and assuming the MBS148 is in the ready state 1708, if a corresponding 911 emergency requestis transmitted to the MBS (via the base station infrastructure) from the911 emergency center or the location center, then the MBS may transitionto a seek state 1712 by performing the following steps:

-   -   (2 a) Communicating with, for example, the 911 emergency        response center via the fixed location base station network to        receive the PN code for the target MS to be located (wherein        this communication is performed using the MS-like transceiver        1512 and/or the MBS operator telephony interface 1524).    -   (2 b) Obtaining a most recent target MS location estimate from        either the 911 emergency center or the location center 142.    -   (2 c) Inputting by the MBS operator an acknowledgment of the        target MS to be located, and transmitting this acknowledgment to        the 911 emergency response center via the transceiver 1512.

Subsequently, when the MBS 148 is in the seek state 1712, the MBS maycommence toward the target MS location estimate provided. Note that itis likely that the MBS is not initially in direct signal contact withthe target MS. Accordingly, in the seek state 1712 the following stepsmay be, for example, performed:

-   -   (3 a) The location center 142 or the 911 emergency response        center may inform the target MS, via the fixed location base        station network, to lower its threshold for soft hand-off and at        least periodically boost its location signal strength.        Additionally, the target MS may be informed to scan for the        pilot channel of the MBS 148. (Note the actions here are not,        actions performed by the MBS 148 in the “seek state”; however,        these actions are given here for clarity and completeness.)    -   (3 b) Repeatedly, as sufficient new MS location information is        available, the location center 142 provides new MS location        estimates to the MBS 148 via the fixed location base station        network.    -   (3 c) The MBS repeatedly provides the MBS operator with new        target MS location estimates provided substantially by the        location center via the fixed location base station network.    -   (3 d) The MBS 148 repeatedly attempts to detect a signal from        the target MS using the PN code for the target MS.    -   (3 e) The MBS 148 repeatedly estimates its own location (as in        other states as well), and receives MBS location estimates from        the location center.

Assuming that the MBS 148 and target MS 140 detect one another (whichtypically occurs when the two units are within 0.25 to 3 miles of oneanother), the MBS enters a contact state 1716 when the target MS 140enters a soft hand-off state with the MBS. Accordingly, in the contactstate 1716, the following steps are, for example, performed:

-   -   (4 a) The MBS 148 repeatedly estimates its own location.    -   (4 b) Repeatedly, the location center 142 provides new target MS        140 and MBS location estimates to the MBS 148 via the fixed        location base infrastructure network.    -   (4 c) Since the MBS 148 is at least in soft hand-off with the        target MS 140, the MBS can estimate the direction and distance        of the target MS itself using, for example, detected target MS        signal strength and TOA as well as using any recent location        center target MS location estimates.    -   (4 d) The MBS 148 repeatedly provides the MBS operator with new        target MS location estimates provided using MS location        estimates provided by the MBS itself and by the location center        via the fixed location base station network.

When the target MS 140 detects that the MBS pilot channel issufficiently strong, the target MS may switch to using the MBS 148 asits primary base station. When this occurs, the MBS enters a controlstate 1720, wherein the following steps are, for example, performed:

-   -   (5 a) The MBS 148 repeatedly estimates its own location.    -   (5 b) Repeatedly, the location center 142 provides new target MS        and MBS location estimates to the MBS 148 via the network of        base stations 122 (152).    -   (5 c) The MBS 148 estimates the direction and distance of the        target MS 140 itself using, for example, detected target MS        signal strength and TOA as well as using any recent location        center target MS location estimates.    -   (5 d) The MBS 148 repeatedly provides the MBS operator with new        target MS location estimates provided using MS location        estimates provided by the MBS itself and by the location center        142 via the fixed location base station network.    -   (5 e) The MBS 148 becomes the primary base station for the        target MS 140 and therefore controls at least the signal        strength output by the target MS.

Note, there can be more than one MBS 148 tracking or locating an MS 140.There can also be more than one target MS 140 to be tracked concurrentlyand each target MS being tracked may be stationary or moving.

MBS Subsystem Architecture

An MBS 148 uses MS signal characteristic data for locating the MS 140.The MBS 148 may use such signal characteristic data to facilitatedetermining whether a given signal from the MS is a “direct shot” or anmultipath signal. That is, in one embodiment, the MBS 148 attempts todetermine or detect whether an MS signal transmission is receiveddirectly, or whether the transmission has been reflected or deflected.For example, the MBS may determine whether the expected signal strength,and TOA agree in distance estimates for the MS signal transmissions.Note, other signal characteristics may also be used, if there aresufficient electronics and processing available to the MBS 148; i.e.,determining signal phase and/or polarity as other indications ofreceiving a “direct shot” from an MS 140.

In one embodiment, the MBS 148 (FIG. 11) includes an MBS controller 1533for controlling the location capabilities of the MBS 148. In particular,the MBS controller 1533 initiates and controls the MBS state changes asdescribed in FIG. 12. Additionally, the MBS controller 1533 alsocommunicates with the location controller 1535, wherein this lattercontroller controls MBS activities related to MBS location and target MSlocation. The location controller 1535 receives data input from an eventgenerator 1537 for generating event records to be provided to thelocation controller 1535. For example, records may be generated fromdata input received from: (a) the vehicle movement detector 1539indicating that the MBS 148 has moved at least a predetermined amountand/or has changed direction by at least a predetermined angle, or (b)the MBS signal processing subsystem 1541 indicating that the additionalsignal measurement data has been received from either the locationcenter 142 or the target MS 140. Note that the MBS signal processingsubsystem 1541, in one embodiment, is similar to the signal processingsubsystem 1220 of the location center 142. may have multiple commandschedulers. In particular, a scheduler 1528 for commands related tocommunicating with the location center 142, a scheduler 1530 forcommands related to GPS communication (via GPS receiver 1531), ascheduler 1529 for commands related to the frequency and granularity ofthe reporting of MBS changes in direction and/or position via the MBSdead reckoning subsystem 1527 (note that this scheduler is potentiallyoptional and that such commands may be provided directly to thedeadreckoning estimator 1544), and a scheduler 1532 for communicatingwith the target MS(s) 140 being located. Further, it is assumed thatthere is sufficient hardware and/or software to appear to performcommands in different schedulers substantially concurrently.

In order to display an MBS computed location of a target MS 140, alocation of the MBS must be known or determined. Accordingly, each MBS148 has a plurality of MBS location estimators (or hereinafter alsosimply referred to as location estimators) for determining the locationof the MBS. Each such location estimator computes MBS locationinformation such as MBS location estimates, changes to MBS locationestimates, or, an MBS location estimator may be an interface forbuffering and/or translating a previously computed MBS location estimateinto an appropriate format. In particular, the MBS location module 1536,which determines the location of the MBS, may include the following MBSlocation estimators 1540 (also denoted baseline location estimators):

-   -   (a) a GPS location estimator 1540 a (not individually shown) for        computing an MBS location estimate using GPS signals,    -   (b) a location center location estimator 1540 b (not        individually shown) for buffering and/or translating an MBS        estimate received from the location center 142,    -   (c) an MBS operator location estimator 1540 c (not individually        shown) for buffering and/or translating manual MBS location        entries received from an MBS location operator, and    -   (d) in some MBS embodiments, an LBS location estimator 1540 d        (not individually shown) for the activating and deactivating of        LBSs 152. Note that, in high multipath areas and/or stationary        base station marginal coverage areas, such low cost location        base stations 152 (LBS) may be provided whose locations are        fixed and accurately predetermined and whose signals are        substantially only receivable within a relatively small range        (e.g., 2000 feet), the range potentially being variable. Thus,        by communicating with the LBS's 152 directly, the MBS 148 may be        able to quickly use the location information relating to the        location base stations for determining its location by using        signal characteristics obtained from the LBSs 152.        Note that each of the MBS baseline location estimators 1540,        such as those above, provide an actual MBS location rather than,        for example, a change in an MBS location. Further note that it        is an aspect of the present invention that additional MBS        baseline location estimators 1540 may be easily integrated into        the MBS location subsystem 1508 as such baseline location        estimators become available. For example, a baseline location        estimator that receives MBS location estimates from reflective        codes provided, for example, on streets or street signs can be        straightforwardly incorporated into the MBS location subsystem        1508.

Additionally, note that a plurality of MBS location technologies andtheir corresponding MBS location estimators are utilized due to the factthat there is currently no single location technology available that isboth sufficiently fast, accurate and accessible in substantially allterrains to meet the location needs of an MBS 148. For example, in manyterrains GPS technologies may be sufficiently accurate; however, GPStechnologies: (a) may require a relatively long time to provide aninitial location estimate (e.g., greater than 2 minutes); (b) when GPScommunication is disturbed, it may require an equally long time toprovide a new location estimate; (c) clouds, buildings and/or mountainscan prevent location estimates from being obtained; (d) in some casessignal reflections can substantially skew a location estimate. Asanother example, an MBS 148 may be able to use triangulation ortrilateralization technologies to obtain a location estimate; however,this assumes that there is sufficient (fixed location) infrastructure BScoverage in the area the MBS is located. Further, it is well known thatthe multipath phenomenon can substantially distort such locationestimates. Thus, for an MBS 148 to be highly effective in variedterrains, an MBS is provided with a plurality of location technologies,each supplying an MBS location estimate.

In fact, much of the architecture of the location engine 139 could beincorporated into an MBS 148. For example, in some embodiments of theMBS 148, the following FOMs 1224 may have similar location modelsincorporated into the MBS:

-   -   (a) a variation of the TCSO FOM 1224 wherein TOA signals from        communicating fixed location BS's are received (via the MBS        transceiver 1512) by the MBS and used for providing a location        estimate;    -   (b) a variation of the artificial neural net based FOMs 1224 (or        more generally a location learning or a classification model)        may be used to provide MBS location estimates via, for example,        learned associations between fixed location BS signal        characteristics and geographic locations;    -   (c) an LBS location FOM 1224 for providing an MBS with the        ability to activate and deactivate LBS's to provide (positive)        MBS location estimates as well as negative MBS location regions        (i.e., regions where the MBS is unlikely to be since one or more        LBS's are not detected by the MBS transceiver);    -   (d) one or more MBS location reasoning agents and/or a location        estimate heuristic agents for resolving MBS location estimate        conflicts and providing greater MBS location estimate accuracy.        For example, modules similar to the analytical reasoner module        1416 and the historical location reasoner module 1424.

However, for those MBS location models requiring communication with thebase station infrastructure, an alternative embodiment is to rely on thelocation center 142 to perform the computations for at least some ofthese MBS FOM models. That is, since each of the MBS location modelsmentioned immediately above require communication with the network offixed location BS's 122 (152), it may be advantageous to transmit MBSlocation estimating data to the location center 142 as if the MBS wereanother MS 140 for the location center to locate, and thereby rely onthe location estimation capabilities at the location center rather thanduplicate such models in the MBS 148. The advantages of this approachare that:

-   -   (a) an MBS is likely to be able to use less expensive processing        power and software than that of the location center;    -   (b) an MBS is likely to require substantially less memory,        particularly for data bases, than that of the location center.

As will be discussed further below, in one embodiment of the MBS 148,there are confidence values assigned to the locations output by thevarious location estimators 1540. Thus, the confidence for a manualentry of location data by an MBS operator may be rated the highest andfollowed by the confidence for (any) GPS location data, followed by theconfidence for (any) location center location 142 estimates, followed bythe confidence for (any) location estimates using signal characteristicdata from LBSs. However, such prioritization may vary depending on, forinstance, the radio coverage area 120. In an one embodiment of thepresent invention, it is an aspect of the present invention that for MBSlocation data received from the GPS and location center, theirconfidences may vary according to the area in which the MBS 148 resides.That is, if it is known that for a given area, there is a reasonableprobability that a GPS signal may suffer multipath distortions and thatthe location center has in the past provided reliable locationestimates, then the confidences for these two location sources may bereversed.

In one embodiment of the present invention, MBS operators may berequested to occasionally manually enter the location of the MBS 148when the MBS is stationary for determining and/or calibrating theaccuracy of various MBS location estimators.

There is an additional important source of location information for theMBS 148 that is incorporated into an MBS vehicle (such as a policevehicle) that has no comparable functionality in the network of fixedlocation BS's. That is, the MBS 148 may use deadreckoning informationprovided by a deadreckoning MBS location estimator 1544 whereby the MBSmay obtain MBS deadreckoning location change estimates. Accordingly, thedeadreckoning MBS location estimator 1544 may use, for example, anon-board gyroscope 1550, a wheel rotation measurement device (e.g.,odometer) 1554, and optionally an accelerometer (not shown). Thus, sucha deadreckoning MBS location estimator 1544 periodically provides atleast MBS distance and directional data related to MBS movements from amost recent MBS location estimate. More precisely, in the absence of anyother new MBS location information, the deadreckoning MBS locationestimator 1544 outputs a series of measurements, wherein each suchmeasurement is an estimated change (or delta) in the position of the MBS148 between a request input timestamp and a closest time prior to thetimestamp, wherein a previous deadreckoning terminated. Thus, eachdeadreckoning location change estimate includes the following fields:

-   -   (a) an “earliest timestamp” field for designating the start time        when the deadreckoning location change estimate commences        measuring a change in the location of the MBS;    -   (b) a “latest timestamp” field for designating the end time when        the deadreckoning location change estimate stops measuring a        change in the location of the MBS; and    -   (c) an MBS location change vector.        That is, the “latest timestamp” is the timestamp input with a        request for deadreckoning location data, and the “earliest        timestamp” is the timestamp of the closest time, T, prior to the        latest timestamp, wherein a previous deadreckoning output has        its a timestamp at a time equal to T.

Further, the frequency of such measurements provided by thedeadreckoning subsystem 1527 may be adaptively provided depending on thevelocity of the MBS 148 and/or the elapsed time since the most recentMBS location update. Accordingly, the architecture of at least someembodiments of the MBS location subsystem 1508 must be such that it canutilize such deadreckoning information for estimating the location ofthe MBS 148.

In one embodiment of the MBS location subsystem 1508 described infurther detail hereinbelow, the outputs from the deadreckoning MBSlocation estimator 1544 are used to synchronize MBS location estimatesfrom different MBS baseline location estimators. That is, since such adeadreckoning output may be requested for substantially any time fromthe deadreckoning MBS location estimator, such an output can berequested for substantially the same point in time as the occurrence ofthe signals from which a new MBS baseline location estimate is derived.Accordingly, such a deadreckoning output can be used to update other MBSlocation estimates not using the new MBS baseline location estimate.

It is assumed that the error with dead reckoning increases withdeadreckoning distance. Accordingly, it is an aspect of the embodimentof the MBS location subsystem 1508 that when incrementally updating thelocation of the MBS 148 using deadreckoning and applying deadreckoninglocation change estimates to a “most likely area” in which the MBS 148is believed to be, this area is incrementally enlarged as well asshifted. The enlargement of the area is used to account for theinaccuracy in the deadreckoning capability. Note, however, that thedeadreckoning MBS location estimator is periodically reset so that theerror accumulation in its outputs can be decreased. In particular, suchresetting occurs when there is a high probability that the location ofthe MBS is known. For example, the deadreckoning MBS location estimatormay be reset when an MBS operator manually enters an MBS location orverifies an MBS location, or a computed MBS location has sufficientlyhigh confidence.

Thus, due to the MBS 148 having less accurate location information (bothabout itself and a target MS 140), and further that deadreckoninginformation must be utilized in maintaining MBS location estimates, afirst embodiment of the MBS location subsystem architecture is somewhatdifferent from the location engine 139 architecture. That is, thearchitecture of this first embodiment is simpler than that of thearchitecture of the location engine 139. However, it important to notethat, at a high level, the architecture of the location engine 139 mayalso be applied for providing a second embodiment of the MBS locationsubsystem 1508, as one skilled in the art will appreciate afterreflecting on the architectures and processing provided at an MBS 148.For example, an MBS location subsystem 1508 architecture may be providedthat has one or more first order models 1224 whose output is suppliedto, for example, a blackboard or expert system for resolving MBSlocation estimate conflicts, such an architecture being analogous to oneembodiment of the location engine 139 architecture.

Furthermore, it is also an important aspect of the present inventionthat, at a high level, the MBS location subsystem architecture may alsobe applied as an alternative architecture for the location engine 139.For example, in one embodiment of the location engine 139, each of thefirst order models 1224 may provide its MS location hypothesis outputsto a corresponding “location track,” analogous to the MBS locationtracks described hereinbelow, and subsequently, a most likely MS currentlocation estimate may be developed in a “current location track”(alsodescribed hereinbelow) using the most recent location estimates in otherlocation tracks. Thus, the location estimating models of the locationcenter 139 and those of the MBS 148 are may be interchanged depending onthe where it is deemed most appropriate for such each such model toreside. Additionally, note that in different embodiments of the presentinvention, various combinations of the location center locationarchitecture and the mobile station architecture may be utilized ateither the location center or the MBS 148. Thus, by providingsubstantially all location estimating computational models at thelocation center 142, the models described here for locating the MBS 148(and equivalently, its incorporated MS 140) can be used for locatingother MSs 140 that are be capable of supporting transmission of wirelesssignal measurements that relate to models requiring the additionalelectronics available at the MBS 140 (e.g., GPS or other satellitesignals used for location).

Further, note that the ideas and methods discussed here relating to MBSlocation estimators 1540 and MBS location tracks, and, the relatedprograms hereinbelow are sufficiently general so that these ideas andmethods may be applied in a number of contexts related to determiningthe location of a device capable of movement and wherein the location ofthe device must be maintained in real time. For example, the presentideas and methods may be used by a robot in a very cluttered environment(e.g., a warehouse), wherein the robot has access: (a) to a plurality of“robot location estimators” that may provide the robot with sporadiclocation information, and (b) to a deadreckoning location estimator.

Each MBS 148, additionally, has a location display (denoted the MBSoperator visual user interface 1558 in FIG. 11) where area maps that maybe displayed together with location data. In particular, MS locationdata may be displayed on this display as a nested collection of areas,each smaller nested area being the most likely area within (any)encompassing area for locating a target MS 140. Note that the MBScontroller algorithm below may be adapted to receive location center 142data for displaying the locations of other MBSs 148 as well as targetMSs 140.

Further, the MBS 148 may constrain any location estimates to streets ona street map using the MBS location snap to street module 1562. Forexample, an estimated MBS location not on a street may be “snapped to” anearest street location. Note that a nearest street location determinermay use “normal” orientations of vehicles on streets as a constraint onthe nearest street location. Particularly, if an MBS 148 is moving attypical rates of speed and acceleration, and without abrupt changesdirection. For example, if the deadreckoning MBS location estimator 1544indicates that the MBS 148 is moving in a northerly direction, then thestreet snapped to should be a north-south running street. Moreover, theMBS location snap to street module 1562 may also be used to enhancetarget MS location estimates when, for example, it is known or suspectedthat the target MS 140 is in a vehicle and the vehicle is moving attypical rates of speed. Furthermore, the snap to street location module1562 may also be used in enhancing the location of a target MS 140 byeither the MBS 148 or by the location engine 139. In particular, thelocation estimator 1344 or an additional module between the locationestimator 1344 and the output gateway 1356 may utilize an embodiment ofthe snap to street location module 1562 to enhance the accuracy oftarget MS 140 location estimates that are known to be in vehicles. Notethat this may be especially useful in locating stolen vehicles that haveembedded wireless location transceivers (MSs 140), wherein appropriatewireless signal measurements can be provided to the location center 142.

MBS Data Structure Remarks

Assuming the existence of at least some of the location estimators 1540that were mentioned above, the discussion here refers substantially tothe data structures and their organization as illustrated in FIG. 13.

The location estimates (or hypotheses) for an MBS 148 determining itsown location each have an error or range estimate associated with theMBS location estimate. That is, each such MBS location estimate includesa “most likely MBS point location” within a “most likely area”. The“most likely MBS point location” is assumed herein to be the centroid ofthe “most likely area.” In one embodiment of the MBS location subsystem1508, a nested series of “most likely areas” may be provided about amost likely MBS point location. However, to simplify the discussionherein each MBS location estimate is assumed to have a single “mostlikely area”. One skilled in the art will understand how to provide suchnested “most likely areas” from the description herein. Additionally, itis assumed that such “most likely areas” are not grossly oblong; i.e.,area cross sectioning lines through the centroid of the area do not havelarge differences in their lengths. For example, for any such “mostlikely area”, A, no two such cross sectioning lines of A through thecentroid thereof may have lengths that vary by more than a factor offive.

Each MBS location estimate also has a confidence associated therewithproviding a measurement of the perceived accuracy of the MBS being inthe “most likely area” of the location estimate.

A (MBS) “location track” is an data structure (or object) having a queueof a predetermined length for maintaining a temporal (timestamp)ordering of “location track entries” such as the location track entries1770 a, 1770 b, 1774 a, 1774 b, 1778 a, 1778 b, 1782 a, 1782 b, and 1786a (FIG. 13), wherein each such MBS location track entry is an estimateof the location of the MBS at a particular corresponding time.

There is an MBS location track for storing MBS location entries obtainedfrom MBS location estimation information from each of the MBS baselinelocation estimators described above (i.e., a GPS location track 1750 forstoring MBS location estimations obtained from the GPS locationestimator 1540, a location center location track 1754 for storing MBSlocation estimations obtained from the location estimator 1540 derivingits MBS location estimates from the location center 142, an LBS locationtrack 1758 for storing MBS location estimations obtained from thelocation estimator 1540 deriving its MBS location estimates from basestations 122 and/or 152, and a manual location track 1762 for MBSoperator entered MBS locations). Additionally, there is one furtherlocation track, denoted the “current location track” 1766 whose locationtrack entries may be derived from the entries in the other locationtracks (described further hereinbelow). Further, for each locationtrack, there is a location track head that is the head of the queue forthe location track. The location track head is the most recent (andpresumably the most accurate) MBS location estimate residing in thelocation track. Thus, for the GPS location track 1750 has location trackhead 1770; the location center location track 1754 has location trackhead 1774; the LBS location track 1758 has location track head 1778; themanual location track 1762 has location track head 1782; and the currentlocation track 1766 has location track head 1786. Additionally, fornotational convenience, for each location track, the time series ofprevious MBS location estimations (i.e., location track entries) in thelocation track will herein be denoted the “path for the location track.”Such paths are typically the length of the location track queuecontaining the path. Note that the length of each such queue may bedetermined using at least the following considerations:

-   -   (i) In certain circumstances (described hereinbelow), the        location track entries are removed from the head of the location        track queues so that location adjustments may be made. In such a        case, it may be advantageous for the length of such queues to be        greater than the number of entries that are expected to be        removed;    -   (ii) In determining an MBS location estimate, it may be        desirable in some embodiments to provide new location estimates        based on paths associated with previous MBS location estimates        provided in the corresponding location track queue.        Also note that it is within the scope of the present invention        that the location track queue lengths may be a length of one.

Regarding location track entries, each location track entry includes:

-   -   (a) a “derived location estimate” for the MBS that is derived        using at least one of:        -   (i) at least a most recent previous output from an MBS            baseline location estimator 1540 (i.e., the output being an            MBS location estimate);        -   (ii) deadreckoning output information from the deadreckoning            subsystem 1527.    -   Further note that each output from an MBS location estimator has        a “type” field that is used for identifying the MBS location        estimator of the output.    -   (b) an “earliest timestamp” providing the time/date when the        earliest MBS location information upon which the derived        location estimate for the MBS depends. Note this will typically        be the timestamp of the earliest MBS location estimate (from an        MBS baseline location estimator) that supplied MBS location        information used in deriving the derived location estimate for        the MBS 148.    -   (c) a “latest timestamp” providing the time/date when the latest        MBS location information upon which the derived location        estimate for the MBS depends. Note that earliest        timestamp=latest timestamp only for so called “baseline entries”        as defined hereinbelow. Further note that this attribute is the        one used for maintaining the “temporal (timestamp) ordering” of        location track entries.    -   (d) A “deadreckoning distance” indicating the total distance        (e.g., wheel turns or odometer difference) since the most        recently previous baseline entry for the corresponding MBS        location estimator for the location track to which the location        track entry is assigned.

For each MBS location track, there are two categories of MBS locationtrack entries that may be inserted into a MBS location track:

-   -   (a) “baseline” entries, wherein each such baseline entry        includes (depending on the location track) a location estimate        for the MBS 148 derived from: (i) a most recent previous output        either from a corresponding MBS baseline location estimator,        or (ii) from the baseline entries of other location tracks (this        latter case being the for the “current” location track);    -   (b) “extrapolation” entries, wherein each such entry includes an        MBS location estimate that has been extrapolated from the (most        recent) location track head for the location track (i.e., based        on the track head whose “latest timestamp” immediately precedes        the latest timestamp of the extrapolation entry). Each such        extrapolation entry is computed by using data from a related        deadreckoning location change estimate output from the        deadreckoning MBS location estimator 1544. Each such        deadreckoning location change estimate includes measurements        related to changes or deltas in the location of the MBS 148.        More precisely, for each location track, each extrapolation        entry is determined using: (i) a baseline entry, and (ii) a set        of one or more (i.e., all later occurring) deadreckoning        location change estimates in increasing “latest timestamp”        order. Note that for notational convenience this set of one or        more deadreckoning location change estimates will be denoted the        “deadreckoning location change estimate set” associated with the        extrapolation entry resulting from this set.    -   (c) Note that for each location track head, it is either a        baseline entry or an extrapolation entry. Further, for each        extrapolation entry, there is a most recent baseline entry, B,        that is earlier than the extrapolation entry and it is this B        from which the extrapolation entry was extrapolated. This        earlier baseline entry, B, is hereinafter denoted the “baseline        entry associated with the extrapolation entry.” More generally,        for each location track entry, T, there is a most recent        previous baseline entry, B, associated with T, wherein if T is        an extrapolation entry, then B is as defined above, else if T is        a baseline entry itself, then T=B. Accordingly, note that for        each extrapolation entry that is the head of a location track,        there is a most recent baseline entry associated with the        extrapolation entry.

Further, there are two categories of location tracks:

-   -   (a) “baseline location tracks,” each having baseline entries        exclusively from a single predetermined MBS baseline location        estimator; and    -   (b) a “current” MBS location track having entries that are        computed or determined as “most likely” MBS location estimates        from entries in the other MBS location tracks.        MBS Location Estimating Strategy

In order to be able to property compare the track heads to determine themost likely MBS location estimate it is an aspect of the presentinvention that the track heads of all location tracks include MBSlocation estimates that are for substantially the same (latest)timestamp. However, the MBS location information from each MBS baselinelocation estimator is inherently substantially unpredictable andunsynchronized. In fact, the only MBS location information that may beconsidered predicable and controllable is the deadreckoning locationchange estimates from the deadreckoning MBS location estimator 1544 inthat these estimates may reliably be obtained whenever there is a queryfrom the location controller 1535 for the most recent estimate in thechange of the location for the MBS 148. Consequently (referring to FIG.13), synchronization records 1790 (having at least a 1790 b portion, andin some cases also having a 1790 a portion) may be provided for updatingeach location track with a new MBS location estimate as a new trackhead. In particular, each synchronization record includes adeadreckoning location change estimate to be used in updating all but atmost one of the location track heads with a new MBS location estimate byusing a deadreckoning location change estimate in conjunction with eachMBS location estimate from an MBS baseline location estimator, thelocation track heads may be synchronized according to timestamp. Moreprecisely, for each MBS location estimate, E, from an MBS baselinelocation estimator, the present invention also substantiallysimultaneously queries the deadreckoning MBS location estimator for acorresponding most recent change in the location of the MBS 148.Accordingly, E and the retrieved MBS deadreckoning location changeestimate, C, have substantially the same “latest timestamp”. Thus, thelocation estimate E may be used to create a new baseline track head forthe location track having the corresponding type for E, and C may beused to create a corresponding extrapolation entry as the head of eachof the other location tracks. Accordingly, since for each MBS locationestimate, E, there is a MBS deadreckoning location change estimate, C,having substantially the same “latest timestamp”, E and C will behereinafter referred as “paired.”

Wireless Location Applications

Such wireless location applications as were briefly described above inreference to the gateway 142 will now be described in further detail.Note that the following location related services are considered withinthe scope of the invention, and such services can, in general, beprovided without use of a gateway 142, albeit, e.g., in a likely morerestricted context wherein not all available wireless locationestimating techniques are utilized, and/or by multiplying the number ofinterfaces to geolocation service providers (e.g., distinct wirelesslocation interfaces are provided directly to each wireless locationservice provider utilized).

Routing Applications

In one noteworthy routing application, hotels and other personal serviceproviders, such as auto rental agencies, hotels, resorts and cruiseships may provide an inexpensive MS 140 that can be used substantiallyonly for contacting: (i) the personal service, (ii) emergency services,and/or (iii) receiving directions to return to the personal service.Accordingly, the MS 140 may be wirelessly located during operations (ii)and (iii) via wireless communications between the MS 140 and a localcommercial wireless service provider wherein a request to locate the MS140 is provided to, e.g., the gateway 142, and the resulting MS locationestimate is: provided to a public safety emergency center (e.g., E911)for dispatching emergency services, or provided to a mapping and routingsystem such as provided by Mapinfo or disclosed in the LeBlanc et. al.patent application filed Jan. 22, 1999 and having U.S. Pat. No.6,236,365 (which is fully incorporated herein by reference) so that theMS 140 user may be routed safely and expeditiously to a predeterminedlocation of the personal service. Note that data representing thelocation of the personal service can be associated with anidentification of the MS 140 so that MS activation for (iii) aboveresults in one or more audio and/or visual presentations of directionsfor directing the user to return to the personal service.

The MS 140 and the MS location providing wireless network (e.g., a CMRS,a PSTN 124 or the Internet 468) may also provide the MS user with theability to explicitly request to be substantially continuously tracked,wherein the MS tracked locations are stored for access by those havingpermission (e.g., the user, parents and/or associates of the user).Additionally, the velocity and/or expected time of arrival at apredetermined destination may be derived from such tracking and may beprovided to the user or his/her associates (e.g., employer, friends,and/or family). Further, note that this tracking and notification ofinformation obtained therefrom may be provided via a commercialtelephony or Internet enabled mobile station, or a mobile station inoperable communication with a short messaging service. For example, theMS registered owner may provide permissions for those able to accesssuch MS tracking information so that such information can beautomatically provided to certain associates and/or provided on requestto certain associates. Additionally, note that the MS 140 and the MSlocation providing wireless network may also allow the MS user todeactivate such MS tracking functionality. In one embodiment, an MS usermay activate such tracking for his/her MS 140 during working hours anddeactivate such tracking during non-working hours. Accordingly, anemployer can then track employee's whereabouts during work hours, whilethe employee is able to retain his/her location privacy when not workingalthough the employer may be still able to contact the employee in caseof an emergency during the employee's non-working time. Note, that thislocation capability and method of obtaining location information aboutan MS user while assuring privacy at other times may be useful forappropriately monitoring in personnel in the military, hospitals,transportation services (e.g., for couriers, bus and taxis drivers),telecommunications personnel, emergency rescue and correctionalinstitution personnel. Further, note that this selective MS locationcapability may be performed in a number of ways. For example, the MS 140may activate and deactivate such tracking by dialing a predeterminednumber (e.g., by manually or speed dialing the number) for switchingbetween activation of a process that periodically requests a wirelesslocation of the MS 140 from, e.g., the location gateway 142. Note thatthe resulting MS location information may be made available to otherusers at a predetermined phone number, Internet address or havingsufficient validation information (e.g., a password). Alternatively, theMS location providing wireless network may automatically activate suchMS tracking for predetermined times of the day and for predetermineddays of the week. Note that this latter embodiment may be particularlyuseful for both tracking employees, e.g., at large construction sites,and, e.g., determining when each employee is at his/her work site. Thus,in this embodiment, the MS location providing wireless network mayprovide database storage of times and days of the week for activationand deactivation of this selective MS tracking capability that isaccessible via, e.g., a network service control point 104 (or othertelephony network control points as one skilled in the art willunderstand), wherein triggers may be provided within the database forgenerating a network message (to, e.g., the gateway 142) requesting thecommencement of tracking the MS 140 or the deactivation of suchtracking. Accordingly, the resulting MS location information may beprovided to an employer's tracking and payroll system so that theemployer is able to determine the actual time an employee arrives at andleaves a work location site.

In another routing related application of the present invention, an MS140 and the MS location providing wireless network may provide the MSuser with functionality to register certain locations so that datarepresenting such locations can be easily accessed for use at a latertime. For example, the MS 140 user may be staying at a hotel in anunfamiliar area. Accordingly, using the present capability of theinvention, the user can request, via his/her MS 140, that his/herlocation at the hotel be determined and registered so that it isavailable at a later time for routing the user back to the hotel. Infact, the user may have personal location registrations of a pluralityof locations in various cities and countries so that when traveling theuser has wireless access to directions to preferred locations such ashis/her hotel, preferred restaurants, shopping areas, scenic areas,rendezvous points, theatres, athletic events, churches, entertainmentestablishments, locations of acquaintances, etc. Note, that suchpersonal location registration information may reside primarily on theuser's subscriber network, but upon the MS user's request, his/herpersonal location registrations may be transmitted to another networkfrom which the user is receiving wireless services as a roamer.Moreover, any new location registrations (or deletions) may beduplicated in the user's personal registration of the user's subscribernetwork. However, in some instances an MS user may wish to retain suchregistered locations only temporarily while the user is in a particulararea; e.g., a predetermined network coverage area. Accordingly, the MSuser may indicate (or such may be the default) that a new personallocation registration be retained for a particular length of time,and/or until a location of the user is outside the area to which suchnew location registrations appear to be applicable. However, prior todeleting any such registrations, the MS user may be queried to confirmsuch deletions. For example, if the MS user has new locationregistrations for the Dallas, Tex. area, and the MS user subsequentlytravels to London, then upon the first wireless location performed bythe MS user for location registration services, the MS user may bequeried as whether to save the new Dallas, Tex. location registrationspermanently, for an particular length of time (e.g. 30 days), or deleteall or selected portions thereof.

Other routing related applications of the present invention are forsecurity (e.g., tracking how do I get back to my hotel safely), and,e.g., sight seeing guided tour where the is interactive depending onfeedback from users

Advertising Applications

Advertising may be directed to an MS 140 according to its location. Inat least some studies it is believed that MS 140 users do not respondwell to unsolicited wireless advertisement whether location based orotherwise. However, in response to certain user queries for locallyavailable merchandise, certain advertisements may be viewed as morefriendly. Thus, by allowing an MS user to contact, e.g., a wirelessadvertising portal by voice or via wireless Internet, and describecertain products or services desired (e.g., via interacting with anautomated speech interaction unit), the user may be able to describe andreceive (at his/her MS 140) audio and/or visual presentations of suchproducts or services that may satisfy such a user's request. Forexample, a user may enter a request: “I need a Hawaiian shirt, who hassuch shirts near here?”

In the area of advertising, the present invention has advantages bothfor the MS user (as well as the wireline user), and for product andservice providers that are nearby to the MS user. For instance, an MSuser may be provided with (or request) a default set of advertisementsfor an area when the MS user enters the area, registers with a hotel inthe area, or makes a purchase in the area, and/or requests informationabout a particular product or service in the area. Moreover, there maybe different collections of advertisements for MS users that arebelieved to have different demographic profiles and/or purposes forbeing in the area. Accordingly, an MS whose location is being determinedperiodically may be monitored by an advertisement wizard such that thiswizard may maintain a collection of the MS user's preferences, and needsso that when the MS user comes near a business that can satisfy such apreference or need, then an advertisement relating to the fulfillment ofthe preference or need may be presented to the MS user. However, it isan aspect of the invention that such potential advertising presentationsbe intelligently selected using as much information about the user as isavailable. In particular, in one embodiment of the invention MS userpreferences and needs may be ordered according to importance. Moreover,such user preferences and needs may be categorized by temporalimportance (i.e., must be satisfied within a particular time frame,e.g., immediately, today, or next month) and by situational importancewherein user preferences and needs in this category are less timecritical (e.g., do not have to be satisfied immediately, and/or within aspecified time period), but if certain criteria are met the user willconsider satisfying such a preference or need. Thus, finding a Chineserestaurant for dinner may be in the temporal importance category whilepurchasing a bicycle and a new pair of athletic shoes may be ordered aslisted here in the situational category. Accordingly, advertisements forChinese restaurants may be provided to the user at least partiallydependent upon the user's location. Thus, once such a restaurant isselected and routing directions are determined, then the advertisingwizard may examine advertisements or other available product inventoriesand/or services that are within a predetermined distance of the route tothe restaurant for determining whether there is product or service alongthe route that could potentially satisfy one of the user's preferencesor needs from the situational importance category. If so, then the MSuser may be provided with the option of examining such product orservice information and registering the locations of user selectedbusinesses providing such products or services. Accordingly, the routeto the restaurant may be modified to incorporate detours to one or moreof these selected businesses. The flowchart of FIGS. 20A and 20Bprovides steps that illustrate the modification (if necessary) of such aroute so that the MS user can visit one or more locations along theroute for accessing one or more additional products or services.

Of course, an MS user's situationally categorized preferences and needsmay allow the MS user to receive unrequested advertising during othersituations as well. Thus, whenever an MS user is moving such anadvertisement wizard (e.g., if activated by the user) may attempt tosatisfy the MS user's preferences and needs by presenting to the useradvertisements of nearby merchants that appear to be directed to suchuser preferences and needs.

Accordingly, for MS user preferences and needs, the wizard will attemptto present information (e.g., advertisements, coupons, discounts,product price and quality comparisons) related to products and/orservices that may satisfy the user's corresponding preference or need:(a) within the time frame designated by the MS user when identified ashaving a temporal constraint, and/or (b) consistent with situationalcriteria provided by the MS user (e.g., item on sale, item is less thana specified amount, within a predetermined traveling distance and/ortime) when identified as having a situational constraint. Moreover, suchinformation may be dependent on the geolocation of both the user and amerchant(s) having such products and/or services. Additionally, suchinformation may be dependent on a proposed or expected user route (e.g.,a route to work, a trip route). Thus, items in the temporal category areordered according to how urgent must a preference or need must besatisfied, while items in the situational category may be substantiallyunordered and/or ordered according to desirableness (e.g., an MS usermight want a motorcycle of a particular make and maximum price, but wanta new car more). However, since items in the situational category may befulfilled by substantially serendipitous circumstances detected by thewizard, various orderings or no ordering may be used. Thus, e.g., if theMS user travels from one commercial area to another, the wizard maycompare a new collection of merchant products and/or services againstthe items on an MS user's temporal and situational lists, and at leastalert the MS user that there may be new information available about auser desired service or product which is within a predeterminedtraveling time from where the user is. Note that such alerts may bevisual (e.g., textual, or iconic) displays, or audio presentationsusing, e.g., synthesized speech (such as “Discounted motorcycles aheadthree blocks at Cydes Cycles”).

Note that the advertising aspects of the present invention may beutilized by an intelligent electronic yellow pages which can utilize theMS user's location (and/or anticipated locations; e.g., due to roadwaysbeing traversed) together with user preferences and needs (as well asother constraints) to both intelligently respond to user requests aswell as intelligently anticipate user preferences and needs. A blockdiagram showing the high level components of an electronic yellow pagesaccording to this aspect of the present invention is shown in FIG. 19.Accordingly, in one aspect of the present invention advertising is userdriven in that the MS user is able to select advertising based onattributes such as: merchant proximity, traffic/parking conditions, theproduct/service desired, quality ratings, price, user merchantpreferences, product/service availability, coupons and/or discounts.That is, the MS user may be able to determine an ordering ofadvertisements presented based on, e.g., his/her selection inputs forcategorizing such attributes. For example, the MS user may requestadvertisements of athletic shoes be ordered according to the followingvalues: (a) within 20 minutes travel time of the MS user's currentlocation, (b) midrange in price, (c) currently in stock, and (d) nopreferred merchants. Note that in providing advertisements according tothe MS user's criteria, the electronic yellow pages may have to makecertain assumptions such as if the MS user does not specify a time forbeing at the merchant, the electronic yellow pages may default the timeto a range of times somewhat longer than the travel time thereby goingon the assumption that MS user will likely be traveling to an advertisedmerchant relatively soon. Accordingly, the electronic yellow pages mayalso check stored data on the merchant (e.g., in the merchant profile &location database of FIG. 19) to assure that the MS user can access themerchant once the MS user arrives at the merchant's location (e.g., thatthe merchant is open for business). Accordingly, the MS user maydynamically, and in real time, vary such advertising selectionparameters for thereby substantially immediately changing theadvertising being provided to the user's MS. For example, the MS displaymay provide an area for entering an identification of a product/servicename wherein the network determines a list of related or complementaryproducts/services. Accordingly, if an MS user desires to purchase awedding gift, and knows that the couple to be wed are planning a trip toAustralia, then upon the MS user providing input in response toactivating a “related products/services” feature, and then inputting,e.g., “trip to Australia” (as well as any other voluntary informationindicating that the purchase is for: a gift, for a wedding, and/or aprice of less than $100.00), then the intelligent yellow pages may beable to respond with advertisements for related products/services suchas portable electric power converter for personal appliances that isavailable from a merchant local (and/or non-local) to the MS user.Moreover, such related products/services (and/or “suggestion”)functionality may be interactive with the MS user. For example, theremay be a network response to the MS user's above gift inquiry such as“type of gift: conventional or unconventional?” Moreover, the networkmay inquire as to the maximum travel time (or distance) the MS user iswilling to devote to finding a desired product/service, and/or themaximum travel time (or distance) the MS user is willing to devote tovisiting any one merchant. Note that in one embodiment of the electronicyellow pages, priorities may be provided by the MS user as to apresentation ordering of advertisements, wherein such ordering may beby: price.

Note that various aspects of such an electronic yellow pages describedherein are not constrained to using the MS user's location. In general,the MS user's location is but one attribute that can be intelligentlyused for providing users with targeted advertising, and importantly,advertising that is perceived as informative and/or addresses currentuser preferences and needs. Accordingly, such electronic yellow pageaspects of the present invention that are not related to a change in theMS user's location over time also apply to stationary communicationstations such home computers wherein, e.g., such electronic yellow pagesare accessed via the Internet. Additionally, the MS user may be able toadjust, e.g., via iconic selection switches (e.g., buttons or toggles)and icon range specifiers (e.g., slider bars) the relevancy and acorresponding range for various purchasing criteria. In particular, oncea parameter is indicated as relevant (e.g., via activating a toggleswitch), a slider bar may be used for indicating a relative or absolutevalue for the parameter. Thus, parameter values may be for:product/service quality ratings (e.g., display given to highestquality), price (low comparable price to high comparable price), traveltime (maximum estimated time to get to merchant), parking conditions.

Accordingly, such electronic yellow pages may include the followingfunctionality:

-   -   (a) dynamically change as the user travels from one commercial        area to another when the MS user's location is periodically        determined such that local merchant's are given preference;    -   (b) routing instructions are provided to the MS user when a        merchant is selected;    -   (c) provide dynamically generated advertising that is related to        an MS user's preferences or needs. For example, if an MS user        wishes to purchase a new dining room set, then such an        electronic yellow pages may dynamically generate advertisements        (e.g., via the ad generation component of the merchant ad        management system of FIG. 19) with dining room sets therein for        merchants that sell them. Note that this aspect of the present        invention can be accomplished by having, e.g., a predetermined        collection of advertising templates (e.g., in the merchant ad        template database, FIG. 19) that are assigned to particular        areas of an MS user's display wherein the advertising        information is selected according to the item(s) that the MS        user has expressed a preference or desire to purchase, and        additionally, according to the user's location, the user's        preferred merchants, and/or the item's price, quality, as well        as coupons, and/or discounts that may be provided. Thus, such        displays may have a plurality of small advertisements that may        be selected for hyperlinking to more detailed advertising        information related to a product or service the MS user desires.        Note that this aspect of the present invention may, in one        embodiment, provide displays (and/or corresponding audio        information) that is similar to Internet page displays. However,        such advertising may dynamically change with the MS user's        location such that MS user preferences and needs for an item(s)        (including services) having higher priority are given        advertisement preference on the MS display when the MS user        comes within a determined proximity of the merchant offering the        item. Moreover, the MS user may be able to dynamically        reprioritize the advertising displayed and/or change a proximity        constraint so that different advertisements are displayed.        Furthermore, the MS user may be able to request advertising        information on a specified number of nearest merchants that        provide a particular category of products or services. For        example, an MS user may be able to request advertising on the        three nearest Chinese restaurants that have a particular quality        rating.    -   (d) information about MS users' preferences and needs may be        supplied to yellow page merchants regarding MS users that reside        and/or travel nearby yellow sage subscriber merchant locations        as described hereinabove.

The following is a high level description of some of the componentsshown in FIG. 19 of an illustrative embodiment of the electronic yellowpages of the present invention.

-   -   a. Electronic yellow pages center: Assists both the users and        the merchants in providing more useful advertising for enhancing        business transactions. The electronic yellow pages center may be        a regional center within the carrier, or (as shown) an        enterprise separate from the carrier. The center receives input        from users regarding preferences and needs which are first        received by the user interface.    -   b. User interface: Receives input from a user that validates the        user via password, voice identification, or other biometric        capability for identifying the user. Note that the        identification of the user's communication device (e.g., phone        number) is also provided. For a user contact, the user interface        does one of: (a) validates the user thereby allowing user access        to further electronic yellow page services, (b) requests        additional validation information from the user, or (c)        invalidates the user and rejects access to electronic yellow        pages. Note that the user interface retrieves user        identification information from the user profile database        (described hereinbelow), and allows a validated user to add,        delete, and/or modify such user identification information.    -   c. User ad advisor: Provides user interface and interactions        with the user. Receives an identification/description of the        user's communication device for determining an appropriate user        communication technique. Note that the user ad advisor may also        query (any) user profile available (using the user's identity)        for determining a preferred user communication technique        supported by the user's communication device. For example, if        the user's communication device supports visual presentations,        then the user ad advisor defaults to visual presentations unless        there are additional constraints that preclude providing such        visual presentations. In particular, the user may request only        audio ad presentations, or merely graphical pages without video.        Additionally, if the user's communication device supports speech        recognition, then the user ad advisor may interact with the user        solely via verbal interactions. Note that such purely verbal        interactions may be preferable in some circumstances such as        when the user can not safely view a visual presentation; e.g.,        when driving. Further note that the user's communication device        may sense when it is electronically connected to a vehicle and        provide such sensor information to the user ad advisor so that        this module will then default to only a verbal presentation        unless the user requests otherwise. Accordingly, the user ad        advisor includes a speech recognition unit (not shown) as well        as a presentation manager (not shown) for outputting ads in a        form compatible both with the functional capabilities of the        user's communication device and with the user's interaction        preference.        -   Note that the user ad advisor communicates: (a) with the            user ad selection engine for selecting advertisements to be            presented to the user, (b) with the user profile database            for inputting thereto substantially persistent user personal            information that can be used by the user ad selection            engine, and for retrieving user preferences such as media            preference(s) for presentations of advertisements, and (c)            with the user preference and needs satisfaction agents for            instantiating intelligent agents (e.g., database triggers,            initiating merchant requests for a product/service to            satisfy a user preference or need) of the user preference &            needs satisfaction agent management system shown in FIG. 19.        -   Also note that in some embodiments of the present invention,            the user ad advisor may also interact with a user for            obtaining feedback regarding: (a) whether the advertisements            presented, the merchants represented, and/or the            products/services offered are deemed appropriate by the            user, and (b) the satisfaction with a merchant with which            the user has interactions. In particular, such feedback may            be initiated and/or controlled substantially by the user            preference and needs satisfaction agent management system            (described hereinbelow).    -   d. User profile database: A database management system for        accessing and retaining user identification information, user        personal information, and identification of the user's        communication device (e.g., make, model, and/or software        version(s) being used). Note that the user profile database may        contain information about the user that is substantially        persistent; e.g., preferences for: language (e.g., English,        Spanish, etc.), ad presentation media (e.g., spoken, textual,        graphical, and/or video), maximum traveling time/distance for        user preferences and needs of temporal importance (e.g., what is        considered “near” to the user), user demographic information        (e.g., purchasing history, income, residential address, age,        sex, ethnicity, marital status, family statistics such as number        of children and their ages), and merchant        preferences/preclusions (e.g., user prefers one restaurant chain        over another, or the user wants no advertisements from a        particular merchant).    -   e. User ad selection engine (also referred to as “user ad        selection” in FIG. 19): This module selects advertisements that        are deemed appropriate to the user's preferences and needs. In        particular, this module determines the categories and        presentation order of advertisements to be presented to the        user. To perform this task, the user ad selection engine uses a        user's profile information (from the user profile database), a        current user request (via the user ad advisor), and/or the        user's current geolocation (via the interface to the location        gateway 142). Thus, for a user requesting the location of an        Italian restaurant within ½ mile of the user's current location,        in a medium price range, and accepting out of town checks, the        user ad selection engine identifies the ad criteria within the        user's request, and determines the advertising categories        (and/or values thereof) from which advertisements are desired.

Note that the user ad selection engine can suggest advertisementcategories and/or values thereof to the user if requested to do so.

When an MS 140 appears to be traveling an extended distance through aplurality of areas (as determined, e.g., by recent MS locations along aninterstate that traverse a plurality of areas), then upon entering eachnew area having a new collection of location registrations (and possiblya new location registration wizard) may be provided. For example, a newdefault set of local location registrations may become available to theuser. Accordingly, the user may be notified that new temporary locationregistrations are available for the MS user to access if desired. Forexample, such notification may be a color change on a video displayindicating that new temporary registrations are available. Moreover, ifthe MS user has a personal profile that also is accessible by a locationregistration wizard, then the wizard may provide advertising for localbusinesses and services that are expected to better meet the MS user'stastes and needs. Thus, if such a wizard knows that the MS user prefersfine Italian food but does not want to travel more than 20 minutes byauto from his/her hotel to reach a restaurant, then advertisements forrestaurants satisfying such criteria will become available to the user.However, MS users may also remain anonymous to such wizards.

Note, that by retaining MS user preferences and needs, if permission isprovided, e.g., for anonymously capturing such user information, thisinformation could be provided to merchants. Thus, merchants can get anunderstanding of what nearby MS user's would like to purchase (and underwhat conditions, e.g., an electric fan for less than $10). Note suchuser's may be traveling through the area, or user's may live nearby.Accordingly, it is a feature of the present invention to providemerchant's with MS user preferences and needs according to whether theMS user is a passerby or lives nearby so that the merchant can bettertarget his/her advertising.

In one embodiment, a single wizard may be used over the coverage area ofa CMRS and the database of local businesses and services changes as theMS user travels from one location registration area to another.Moreover, such a wizard may determine the frequency and when requestsfor MS locations are provided to the gateway 142. For example, suchdatabases of local businesses and services may be coincident with LATAboundaries. Additionally, the wizard may take into account the directionand roadway the MS 140 is traveling so that, e.g., only businesseswithin a predetermined area and preferably in the direction of travel ofthe MS 140 are candidates to have advertising displayed to the MS user.

Points of Interest Applications

The invention can used for sight seeing guided tours where the inventionis interactive depending on feedback from users. Such interactivitybeing both verbal descriptions and directions to points of interest.

Security Applications

The invention may provide Internet picture capture with real time voicecapture and location information for sightseeing, and/or security.

The foregoing description of preferred embodiments of the presentinvention has been provided for the purposes of illustration anddescription. It is not intended to be exhaustive or to limit theinvention to the precise forms disclosed herein. Modifications andvariations commensurate with the description herein will be apparentthose skilled in the art and are intended to be within the scope of thepresent invention to the extent permitted by the relevant art. Theembodiments provided are for enabling others skilled in the art tounderstand the invention, its various embodiments and modifications asare suited for uses contemplated. It is intended that the scope of theinvention be defined by the following claims and their equivalents.

1. A method for locating mobile units, wherein for each of the mobileunits, wireless signal measurements are obtained from transmissionsbetween the mobile unit and a plurality of terrestrial communicationstations, and wherein the mobile unit is independently moveable fromeach of the communication stations, and each of said communicationsstations includes one or more of: a transmitter for transmittingwireless signals to the mobile units, and a receiver for receivingwireless signals from the mobile units, comprising: first requesting alocation of a first of the mobile units; second requesting a location ofa second of the mobile units; wherein for at least the first mobileunit, one of the following location techniques (A-1) through (A-5) isused for locating the first mobile unit, and for at least the secondmobile unit, a different one of the following location techniques (A-1)through (A-5) is used for locating the second mobile unit; (A-1) a firsttechnique for recognizing a pattern of wireless signal transmissioncharacteristics, wherein said pattern of characteristics is indicativeof a plurality of wireless signal transmission paths between the mobileunit and each of one or more of the communication stations; (A-2) asecond technique for estimating a location of said mobile unit, whereinthe following steps (A-2-1) through (A-2-3) are performed: (A-2-1)determining a time difference of arrival (TDOA) location estimate ofsaid mobile unit based upon timing information of signals transmittedbetween the mobile unit and the communication stations; (A-2-2)determining a timing advance (TA) location estimate for the mobile unitand at least one of the communication stations; and (A-2-3) determininga location of the mobile unit using the TDOA location estimate, and theTA location estimate; (A-3) a third technique for estimating a locationof said mobile unit, wherein the following steps (A-3-1) through (A-3-3)are performed: (A-3-1) obtaining a first measurement for a time oftravel of a transmission transmitted by a wireless transmitter notsupported on the earth's surface, wherein the transmission is receivedat the mobile unit; (A-3-2) obtaining a second measurement for a time oftravel of a transmission communicated between the mobile unit, and oneof the communication stations (CS) of a cell based communications systemproviding wireless two-way communication with a plurality of additionalmobile units; and (A-3-3) determining a position of said mobile unitusing at least the first measurement to determine a geographical extentfrom the transmitter, and the second measurement to determine ageographical extent from the communication station CS; (A-4) a fourthtechnique for estimating a location of said mobile unit using values ofat least one location related characteristic of signals communicatedbetween the mobile unit and the communication stations, wherein thefollowing steps (A-4-1) through (A-4-10) are performed: (A-4-1)estimating a channel power profile for each of M samples received at oneof the communication stations; (A-4-2) selecting a first set of Nsamples from the M samples; (A-4-3) performing incoherent integrationfor said estimated channel power profiles for said first set of Nsamples to form a first integrated signal; (A-4-4) if a quality level ofthe first integrated signal with respect to signal to noise is less thana predetermined threshold, selecting another sample from the M samples;(A-4-5) performing incoherent integration for the estimated channelpower profiles for said first set of N samples and said another sampleto form a second integrated signal; (A-4-6) if a quality level of saidsecond integrated signal with respect to signal to noise is greater thanor equal to said predetermined threshold, determining a location relatedsignal characteristic value for a maximum level of the second integratedsignal; (A-4-7) using the signal characteristic value to provide acorresponding entry in a frequency of occurrence data structure; (A-4-8)selecting a second set of N samples from the M samples; (A-4-9)repeating all of the steps (A-4-3) through (A-4-7) for the second set ofN samples; and (A-4-10) determining a preferred value for the signalcharacteristic from the frequency of occurrence data structure; and(A-5) a fifth technique for estimating a location of said mobile unit,wherein the following steps (A-5-1) and (A-5-2) are performed: (A-5-1)determining a joint probability from location related informationobtained from each of two different location providing informationsources; and (A-5-2) using the joint probability to determine a locationestimate of the mobile unit; combining the values indicative ofamplitude and phase for two tone components of signals received at thecommunication stations to determine the position of the mobile unit;first obtaining a first information of a location of the first mobileunit; second obtaining second information of a location of the secondmobile unit; first obtaining first additional information using: (a) thefirst information, and (b) information for a first destinationdetermined using an input for identifying a first one or more geographiclocations of interest, wherein when at least a first geographicalcondition related to both the location of the first mobile unit and theinformation for the first destination is satisfied, the first additionalinformation includes information for a user of the first mobile unit toaccess the first destination; second obtaining second additionalinformation using: (a) the second information, and (b) information for asecond destination determined using an input for identifying a secondone or more geographic locations of interest, wherein when at least asecond geographical condition related to both the location of the secondmobile unit and the information for the second destination is satisfied,the second additional information includes information for a user of thesecond mobile unit to access the second destination; first transmittingthe first additional information to the first mobile unit; and secondtransmitting the second additional information to the second mobileunit.
 2. The method of claim 1, further including for a third mobileunit, third requesting a location of the third mobile unit, wherein thethird request results in an activation of a sixth technique forestimating a location of said mobile unit, wherein the following steps(A-6-1) through (A-6-4) are performed: (A-6-1) receiving globalpositioning system satellite (GPS) signals from a plurality of globalpositioning system satellites; (A-6-2) receiving a plurality of cellularposition signals that contain data in a GPS-like format; (A-6-3)calculating the geographic position of the third mobile unit usingsignals received from a requisite number of satellites of a globalpositioning system signals when the requisite number of the plurality ofglobal positioning system satellites are in view of a global positioningsystem receiver coupled to the third mobile unit; and (A-6-4)calculating the geographic position of the third mobile unit usingmeasurements of wireless transmissions between the third mobile unit andboth said received plurality of cellular position signals andsubstantially all of said received global positioning system satellitesignals when the requisite number of the plurality of global positioningsystem satellites are not in view of the global positioning systemreceiver.
 3. The method of claim 1, wherein the first and second mobileunits are the same.
 4. The method of claim 1, wherein the first andsecond mobile units are different.
 5. The method of claim 1, furtherincluding a step of transmitting routing information to at least thefirst mobile unit, wherein the routing information is for a route to thefirst destination.
 6. The method of claim 1, further including a step ofselecting advertising information to transmit to at least one of thefirst and second mobile units, wherein the advertising informationidentifies a corresponding one of the first and second destinations. 7.An apparatus for locating mobile units wherein for each of the mobileunits, wireless signal measurements are obtained from transmissionsbetween the mobile unit and a plurality of terrestrial communicationstations, and wherein the mobile unit is independently moveable fromeach of the communication stations, and each of said communicationsstations includes one or more of: a transmitter for transmittingwireless signals to the mobile units, and a receiver for receivingwireless signals from the mobile units, comprising: an interface forrequesting a location of a first of the mobile units, and for requestinga location of a second of the mobile units; wherein for at least thefirst mobile unit, the interface transmits activation information forone of the following location techniques (A-1) through (A-5) used forlocating the first mobile unit, and for at least the second mobile unit,the interface transmits activation information to a different one of thefollowing location techniques (A-1) through (A-5) used for locating thesecond mobile unit; (A-1) a first technique for recognizing a pattern ofwireless signal transmission characteristics, wherein said pattern ofcharacteristics is indicative of a plurality of wireless signaltransmission paths between the mobile unit and each of one or more ofthe communication stations; (A-2) a second technique for estimating alocation of said mobile unit, wherein the following steps (A-2-1)through (A-2-3) are performed: (A-2-1) determining a time difference ofarrival (TDOA) location estimate of said mobile unit based upon timinginformation of signals transmitted between the mobile unit and thecommunication stations; (A-2-2) determining a timing advance (TA)location estimate for the mobile unit and at least one of thecommunication stations; and (A-2-3) determining a location of the mobileunit using the TDOA location estimate, and the TA location estimate;(A-3) a third technique for estimating a location of said mobile unit,wherein the following steps (A-3-1) through (A-3-3) are performed:(A-3-1) obtaining a first measurement for a time of travel of atransmission transmitted by a wireless transmitter not supported on theearth's surface, wherein the transmission is received at the mobileunit; (A-3-2) obtaining a second measurement for a time of travel of atransmission communicated between the mobile unit, and one of thecommunication stations (CS) of a cell based communications systemproviding wireless two-way communication with a plurality of additionalmobile units; and (A-3-3) determining a position of said mobile unitusing at least the first measurement to determine a geographical extentfrom the transmitter, and the second measurement to determine ageographical extent from the communication station CS; (A-4) a fourthtechnique for estimating a location of said mobile unit using values ofat least one location related characteristic of signals communicatedbetween the mobile unit and the communication stations, wherein thefollowing steps (A-4-1) through (A-4-10) are performed: (A-4-1)estimating a channel power profile for each of M samples received at oneof the communication stations; (A-4-2) selecting a first set of Nsamples from the M samples; (A-4-3) performing incoherent integrationfor said estimated channel power profiles for said first set of Nsamples to form a first integrated signal; (A-4-4) if a quality level ofthe first integrated signal with respect to signal to noise is less thana predetermined threshold, selecting another sample from the M samples;(A-4-5) performing incoherent integration for the estimated channelpower profiles for said first set of N samples and said another sampleto form a second integrated signal; (A-4-6) if a quality level of saidsecond integrated signal with respect to signal to noise is greater thanor equal to said predetermined threshold, determining a location relatedsignal characteristic value for a maximum level of the second integratedsignal; (A-4-7) using the signal characteristic value to provide acorresponding entry in a frequency of occurrence data structure; (A-4-8)selecting a second set of N samples from the M samples; (A-4-9)repeating all of the steps (A-4-3) through (A-4-7) for the second set ofN samples; and (A-4-10) determining a preferred value for the signalcharacteristic from the frequency of occurrence data structure; and(A-5) a fifth technique for estimating a location of said mobile unit,wherein the following steps (A-5-1) and (A-5-2) are performed: (A-5-1)determining a joint probability from location related informationobtained from each of two different location providing informationsources; and (A-5-2) using the joint probability to determine a locationestimate of the mobile unit; combining the values indicative ofamplitude and phase for two tone components of signals received at thecommunication stations to determine the position of the mobile unit; acommon interface for first receiving first information of a location ofthe first mobile unit, and for second receiving second information of alocation of the second mobile unit; one or more common components,including at least a presentation component for providing, for each of aplurality of users, a corresponding presentation for identifying one ormore corresponding geographic locations of interest to the user,wherein: (a) information for: (a) a first location determined using aninput for identifying one or more geographic locations of interest, and(b) the first information are used by the one or more common componentsto determine first additional information related to the first location,wherein when at least one geographical condition related to both thelocation of the first mobile unit and the first location is satisfied,the first additional information includes information for a user of thefirst mobile unit to access the first location; and (b) information for:(a) a second destination determined using an input for identifying oneor more geographic locations of interest, and (b) the second informationare used by the one or more common components to determine secondadditional information, wherein when at least one geographical conditionrelated to both the location of the second mobile unit and the secondlocation is satisfied, the second additional information includesinformation for a user of the second mobile unit to access the secondlocation; first transmitting the first additional information to thefirst mobile unit for presenting to the user of the first mobile unit;and second transmitting the second additional information to the secondmobile unit for presenting to the user of the second mobile unit.
 8. Theapparatus of claim 7, wherein the first and second mobile units are thesame.
 9. The apparatus of claim 7, wherein the first and second mobileunits are different.
 10. The apparatus of claim 7, wherein the one ormore components perform a step of determining routing information totransmit to at least one of the first and second mobile units, whereinthe routing information is for a route to a corresponding one of thefirst and second destinations.
 11. The apparatus of claim 7, wherein theone or more components perform a step of selecting advertisinginformation to transmit to at least one of the first and second mobileunits, wherein the advertising information identifies a correspondingone of the first and second destinations.
 12. The method of claim 1,wherein the first geographical condition includes information fordetermining whether the first destination is: (a1) within one of: aspecified travel distance of the location of the first mobile unit, or aspecified geographical area of the location of the first mobile unit,(a2) within a specified expected elapsed time of travel from thelocation of the first mobile unit; and (a3) nearer to the location ofthe first mobile unit than at least one other destination for accessingan instance of the at least one desired item.
 13. The method of claim 1,wherein the first geographic locations interest include at least onelocation for accessing a product or service identified as of interestprior to the step of first requesting.